Patterns of online impersonation violence and mental health consequences among women in Bangladesh

Patterns of online impersonation violence and mental health consequences among women in Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Patterns of online impersonation violence and mental health consequences among women in Bangladesh Nowshin Islam, Mahmudur Rahman Saidy, Nasir Uddin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8364676/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Digital connectivity has spread rapidly to provide new access to empowering women in education, employment, and civil engagement while concurrently placing them under increased risks of violence against women (VAW) online. While Online Violence against Women (OVAW) has often been noted globally as a non-communicable neglected public health problem, empirical knowledge for Bangladesh is sparse, disjointed, and mostly on students. This research employs an integrated General Strain–Intersectional Feminism theory as its theoretical framework, extending global OVAW theory by revealing how patriarchal honor rules in Bangladesh exacerbate online abuse into distinctly severe forms of reputational strain. To address these gaps, an exploratory sequential mixed-methods studies were applied to synthesize semi-structured interviews with a cross-sectional online questionnaire (N = 202) that was conducted from July to August 2024. The statistical analyses also reveal significant links between the prevalence and severity of OVAW and women’s internet use intensity, place of residence, and employment status. Women living in rural areas and those engaged in paid work were found to be disproportionately affected. By distinguishing among different forms of online abuse and tracing their varied psychological, behavioral, and physical impacts, this study offers new insight into the scope and consequences of OVAW in low- and middle-income settings. Findings indicate that impersonation and hate speech were most frequent modes of OVAW, with nearly two out of every five young women being subjected to impersonation and more than a quarter being subjected to hate speech. Moreover, psychological harms were most prominent: depression and social withdrawal being reported by more than 60% of respondents, with numerous respondents also reporting erosion of trust in others. These findings suggest that OVAW in Bangladesh is not simply an extension of global trends but is sustained by entrenched patriarchal norms, persistent stigma, and limited institutional safeguards. It calls for comprehensive policy measures combining digital literacy initiatives, accessible mental health services, and stronger accountability frameworks to foster a safer and more equitable digital environment for women. Online Violence Against Women (OVAW) Digital Gender-Based Violence Bangladesh Patriarchy Cyber Harassment Psychological Impact Digital Literacy Introduction The digital revolution has fundamentally transformed connectivity, creating new opportunities for women to attain education, enhance economic productivity, and engage in civic action (Bansal et al., 2024; Henry et al., 2024). Despite the revolution's many beneficial outcomes, the prevalence of violence against women (VAW) on the internet (OVAW), a kind of gender violence committed over digital communications networks, has increased (Gámez-Guadix et al., 2023; Woodlock, 2023). OVAW encompasses many violent behaviors such as stalking, hate speech, sexualized threats, impersonation, and non-consensual image sharing (Polyzoidou, 2024; Mukred, 2024; UNFPA, 2024). In the wider literature, this phenomenon is also referred to as technology-facilitated gender-based violence (TfGBV). Rooted in patriarchal power relations, OVAW exploits the anonymity and borderless reach of digital platforms to reproduce and amplify offline gender inequities, thereby posing serious risks to women’s psychological, behavioral, and physical well-being (Citron, 2014; Henry & Powell, 2018; UN Women, 2021). Increasingly, it is recognized not only as a neglected public health emergency but also as a significant impediment to achieving gender-equitable development (Stöckl, 2024; Felten, 2023). Because offenders frequently get away with it, online violence has spread throughout the world and has no boundaries. Between 16% and 58% of women and girls globally are thought to have personally experienced OVAW. OVAW is a systematic silencing of women, exclusion from digital venues, and disturbance of their agency; it cannot be dismissed as simple online wrongdoing. Beyond short-term discomfort, OVAW can have long-term negative effects such despair, anxiety, PTSD, and social disengagement. Additionally, empirical studies show that OVAW exacerbates gender disparities already present in offline sociocultural contexts and is linked to a decline in mental health. These international trends intersect in Bangladesh with nationally based socio-cultural forces wherein female "honor" norms and victim-blaming compound trauma, deter reporting, and enable perpetrator impunity. The combination of transnational online misogyny with patriarchal Bangladeshi institutions-grounded in religious conservatism and family honor codes-intensifies the incidence of harm undercutting female digital participation and mental well-being. However, studies of OVAW remain few in number, with dominant studies of OVAW being unipolar and focused on only select sections such as students alone in Bangladesh. For example, Mridha, Ashrafuzzaman, and Sara (2024) studied cyberbullying of girl students and documented significant social and mental effects. However, for larger demographic, social, and geographical settings than student groups, studies of differentiated risk of OVAW are sparse. Previous work has tended to focus on adolescents (Monni & Sultana, 2016) or considered cybercrime as a generic category (Ahmed et al., 2017). Such studies shed little light on how targeted types of OVAW—such as impersonation versus hate speech—generate different harms by occupations or regions (Mridha et al., 2024). Further, variables such as internet usage frequency, rural/urban residence, and employment status have been reported but remain little studied as risk determinants. Underreporting, fortified by culture- and stigma-enforced blame, persists in hiding the extent of harm (Islam & Rahman, 2023). Individually, these gaps reflect how OVAW not only threatens Bangladeshi women's well-being but also presents a broader challenge to Bangladesh's people-centered vision of inclusive, people-centered advancement. The current study tries to fill these gaps by adopting an exploratory sequential mixed-methods design, wherein the qualitative interviews were conducted first to identify the dominant categories of OVAW, and such insight informed the design of a quantitative survey. Throughout this paper, the terms TfGBV and OVAW are used interchangeably, but for clarity and consistency, the term OVAW has been used throughout. This study furthers theory by testing the boundary conditions of GST within a high-stigma, low enforcement LMIC; it demonstrated that impersonation acts as a ‘reputational strain’ magnified by honor norms, whereas Intersectional Feminism showed that rural/employed women are intersectional risk nodes-a finding that leaves many universal OVAW models challenged. The current study aims to map the most common forms of OVAW in Bangladesh; assess their impact on psychological, behavioral, and physical parameters; and analyze how these vary by employment status, geographic location, and internet use intensity. The current study aims to map the most common forms of OVAW in Bangladesh; assess their impact on psychological, behavioral, and physical parameters; and analyze how these vary by employment status, geographic location, and internet use intensity. Overall, it seeks to develop new empirical evidence that places OVAW in both national and global contexts, identifying it as a pressing human rights and development concern that needs to be addressed to advance gender-equitable digital citizenship and people-centered development in Bangladesh. Objectives The main objectives of this research are to To identify the predominant forms of online violence against women (OVAW) experienced by young women in Bangladesh; To assess the extent to which cultural stigma amplifies mental health impacts beyond global patterns; To analyze the influence of key socio-structural factors — intensity of internet use, rural/urban residence, and employment status — on the prevalence and severity of OVAW. Literature Review and Theoretical Framework 1. Conceptualising Online Violence Against Women (OVAW) Conventionally, OVAW refers to gendered abuse, harassment, or exploitation enacted through digital technologies and platforms. Contributing scholars variously use a range of overlapping terms—such as technology-facilitated gender-based violence (TfGBV), cyber-VAWG, and technology-facilitated sexual violence (TFSV)—in an effort to capture different emphases, but all agree that online spaces mirror and often intensify patriarchal power structures (Henry & Powell, 2015, 2018). Research clearly demonstrates that such practices extend far beyond everyday "incivility." They amount to forms of structural violence characterized by persistence, scalability, algorithmic amplification, and heightened visibility, allowing coercive control to permeate digital publics (Citron, 2014; Henry & Powell, 2018; Jane, 2017). More recently, studies of platform governance have similarly suggested that the moderation of content and the design of features are important for determining how OVAW is both experienced and problematized. Policy choices and technology affordances may either intervene helpfully or helplessly to heighten or diminish harm, highlighting rights-based, victim-oriented approaches to the development of safer internet environments (Blackwell, Lo, & Marwick, 2023; Suzor et al., 2019). 2. Types and Classifications of OVAW Previous studies have investigated the causes and consequences of OVAW and compared the traditional violence against women with OVAW (Khan et al., 2023; Rahman & Hasan, 2018; Filice et al., 2022; Henry & Powell, 2015). Khan et al. (2023) argue that victims of OVAW may experience long-term effects that range from financial loss to mental or emotional stress and, in certain cases, trouble finding housing and employment. Rahman and Hasan (2018) reveal that there are five main reasons why emotional violence occurs in public, and they are patriarchy, family values, gendered socialization, societal standards, and morals. In digital spaces, causes of abuse include pornography addiction and easy access to platforms where offenders evade punishment (Filice et al., 2022). Therefore, the consecutive effect of OVAW has long-term impacts on victims’ mental health. Henry & Powell (2015) argue that both traditional and online violence are forms of gender-based violence, stemming from patriarchal structures that seek to dominate or harm women. Although these scholars examined the causes and consequences of OVAW in contexts such as the USA and Middle East, they overlooked the specific consequences of OVAW among young educated women in Bangladesh and failed to explore differences across urban–rural or occupational divides. 3. Global Prevalence and Trends While there has been an increased awareness of the issue of online violence against women (OVAW), estimating the true extent of the problem can be problematic. The lack of consistent definitions of OVAW, the variability of the ways that researchers conduct surveys, and the fact that many victims of OVAW do not report their experiences because they are fearful of further victimization and/or are afraid of being ostracized by society (Sardinha et al., 2022; Gámez-Guadix et al., 2019), all create barriers to understanding how widespread the phenomenon of OVAW is worldwide. Despite the problems associated with measuring the global prevalence of OVAW, numerous regional and international studies have reported high levels of exposure to various forms of online violence against women. Additionally, the literature indicates that the prevalence of online violence varies based on the platform used, the country of origin, the individual's age, and cultural context (Henry et al., 2023; Bansal et al., 2024). A recent study of the prevalence of technology-facilitated gender-based violence among girls and women in low- and middle-income countries in Asia estimated that between 14% and 75% of girls and women have experienced some form of OVAW during their lives. In particular, girls and women who were between 15 and 25 years old at the time they first accessed an image-based platform were found to have higher levels of OVAW than other individuals (Bansal et al., 2024). There is growing research to indicate that women in public roles experience greater amounts of OVAW than others. According to a number of surveys, 20-73% of female politicians and journalists have experienced severe online abuse, which can result in self-censorship and a reduction in their ability to participate in the democratic process (Kuper & Wachter, 2023; Posetti et al., 2022). Younger women, women who identify as sexual and/or gender minorities, and women who hold public positions are among the most vulnerable to OVAW, while women in lower-middle-income countries are more likely to experience both higher levels of OVAW and more severe OVAW, due to a combination of cultural factors, such as stigma related to patriarchy, inadequate legal frameworks, insufficient responsibility of social media platforms for OVAW, and fragmented responses to OVAW from institutions (UN Women, 2023; Sheikh & Rogers, 2024). 4. Psychological, Behavioral, and Health Consequences The majority of empirical research demonstrates a strong positive association between experiencing online violence and negative mental health outcomes. Specifically, research has demonstrated that girls and women who experience online violence are more likely to suffer from depression, anxiety, PTSD, and decreased self-esteem (Fardouly et al., 2023; Caridade et al., 2022). Many of the mental health consequences of OVAW are accompanied by corresponding behavioral changes, such as withdrawal from online communities, self-censorship, and avoidance of public engagement, which serve to exacerbate pre-existing gender inequality in areas such as education, employment, and politics (Celuch et al., 2023; Posetti et al., 2022). Research has also demonstrated that physical health consequences, including sleep disturbances caused by stress, chronic headache pain, and unexplained somatic symptoms, are common among women who experience online violence (Hegarty et al., 2021; Worsley et al., 2023). Furthermore, longitudinal and cross-sectional studies have established that repeated exposure to OVAW increases the likelihood of developing a traumatic response and leads to a heightened risk of long-term disengagement from digital publics (Reed et al., 2022; Lewis et al., 2024). Overall, the body of research on OVAW provides clear evidence that it constitutes a serious threat to the mental health and well-being of girls and women, and that it serves as a structural barrier to achieving gender equality in virtually every area of public and private life (UN Women, 2023; Ornstein et al., 2023). 5. Scenario in Lower Middle Income Countries (LMICs) and Bangladesh In LMICs, OVAW is compounded by patriarchal norms, stigma, and weak institutions. Early studies in Bangladesh focused on either adolescents or broad trends in cybercrime. More recently, studies looking at university students reported severe emotional, psychological, and social consequences. However, these studies have limitations: most relied on self-selective samples of students, used the “cyberbullying” umbrella concept, and rarely distinguished offense types. Few studies assessed the role of internet use intensity, urban–rural residence, and occupational status factors that are highly relevant to exposure and coping. The current study addresses such gaps by explicitly disaggregating types of OVAW and exploring variation across these contextual variables. Despite global attention to OVAW, important gaps remain in Bangladesh. First, studies often treat OVAW generically, without distinguishing among offense types, though impersonation, hate speech, and doxing differ in prevalence and impact (Ahmed et al., 2017; Monni & Sultana, 2016). Second, while research documents depression, anxiety, or social withdrawal, these effects are rarely studied together, leaving the interplay between mental, behavioral, and physical outcomes underexplored (Vandenbosch & van Oosten, 2017; Mridha et al., 2024). Third, contextual variation—urban vs. rural residence, internet use intensity, and occupational status—remains largely absent from scholarship (Sheikh et al., 2023). Finally, most studies use either small-scale qualitative data or broad cross-sectional surveys, limiting both generalizability and depth. Few have applied mixed-methods designs to bridge these gaps. This study addresses these shortcomings by examining disaggregated forms of OVAW, linking them with varied outcomes, and situating them within Bangladesh’s distinct sociocultural landscape. While there have been previous global and Bangladeshi studies reporting the prevalence and harms of OVAW, most have treated these as extensions of existing gender-based violence frameworks without explaining how local patriarchal systems shape their digital expressions. Very few existing models account for how norms around "honor" transform digital harassment into reputational or relational strain or how such effects vary across rural and occupational contexts. In an effort to fill these theoretical and contextual gaps, this study combines General Strain Theory with Intersectional Feminism in conceptualizing OVAW as an honor-mediated form of digital strain. This approach not only extends GST to non-Western patriarchal settings but also presents a culturally located explanation of how online abuse reproduces structural inequalities within the context of low- and middle-income countries like Bangladesh. Theoretical Frameworks General Strain Theory (GST) and Intersectional Feminism are used as complementary theoretical frameworks in this study to account for OVAW in Bangladesh. The theories are chosen for their promise in explaining the psychological, behavioral, and structural aspects of OVAW, consistent with the aims of the research to determine common forms of OVAW, measure their effects, and determine social and demographic differences. GST accounts for how OVAW produces individual-level strains that result in psychological and behavioral outcomes, and Intersectional Feminism positions these outcomes in the patriarchal and intersectional sociocultural setting of Bangladesh. Combined, they provide a strong basis for informing the methodology, explaining expected findings, and guiding policy interventions. General Strain Theory, formulated by Agnew (1992, 2006), argues that strains—events or conditions that are viewed as negative, like the inability to attain desired goals, losing positive stimuli, or experiencing negative stimuli—lead to negative emotions like depression, anxiety, or fear. Such emotions lead to coping mechanisms, like withdrawal or avoidance, as the individual tries to deal with or escape the strain. Strains are of three types: (a) failure to attain goals (e.g., loss of social standing), (b) blockage of opportunity to gain positive stimuli (e.g., loss of credibility), and (c) presentation of aversive stimuli (e.g., harassment). In OVAW, strains are caused by activities such as impersonation, which threatens personal reputation, or hate speech, which generates fear of social disapproval. In Bangladesh, sociocultural norms of female "honor" and victim-blaming compound these pressures, amplifying psychological distress and social retreat (Islam & Rahman, 2023). For instance, a young female victim of online harassment can become fearful or restrict her online activities to prevent being targeted again, illustrating GST's strain-coping pathway. GST is core to this research inasmuch as it underpins the aim to explore OVAW's behavioral and psychological effects, informing survey and interview questions on mental health (e.g., depression) and behavioral reactions (e.g., avoidance), and offering a framework for explaining how various forms of OVAW have disparate effects. Intersectional Feminism, building on Crenshaw's (1989) theorization and developed further by researchers of technology-facilitated violence (Henry & Powell, 2018), theorizes OVAW as an expression of patriarchal power relations, informed by intersecting systems of oppression, such as gender, socioeconomic status, and geographical location. It contends that online spaces mirror offline gender inequalities, compounded by anonymity and access, which facilitate gendered abuse. In Bangladesh, patriarchal values upheld by religious conservatism and honor culture compound the effect of OVAW through exacerbating victim-blaming and deterring reporting (Islam & Rahman, 2023). Intersectionality also highlights the ways in which dimensions such as rural/urban locality or working status condition vulnerability. For example, rural women are likely to be more vulnerable on account of lower digital literacy, whereas working women might be targeted for defying traditional gender expectations. This viewpoint legitimates the study's aim to investigate demographic differences in OVAW experiences, guiding the incorporation of variables such as occupation and residence in data gathering and highlighting structural obstructions, such as the lack of cybercrime legislation, that facilitate abuse. Integrated Framework and Applicability to the Study GST and Intersectional Feminism constitute a unified framework on individual and systemic levels of OVAW. GST specifies how OVAW strains, e.g., reputational damage or fear of stigma, are transduced to psychological distress and behavioral reactions, and Intersectional Feminism specifies how patriarchal norms and intersectional conditions like rural dwelling or work status shape these effects. The model is consistent with this study's aims: operationalizing OVAW types (Intersectional Feminism), measuring their effects (GST), and examining demographic variation (Intersectional Feminism). It directs the mixed-methods design to inform qualitative interviews for collecting strain-related experiences (GST) and quantitative surveys for investigating demographic patterns (Intersectional Feminism) (Creswell & Plano Clark, 2018). Thematic analysis of interviews will determine strain and patriarchal themes (Braun & Clarke, 2019), whereas quantitative analyses will examine contextual influences. The model guides policy suggestions, for instance, mental health care for strains (GST) and setting-based interventions for marginalized groups (Intersectional Feminism), to a sophisticated understanding of OVAW as well as interventions for gender-equitable digital citizenship in Bangladesh. Research Methodology This study is thus designed in an exploratory sequential mixed-methods manner to capture both the depth of the lived experiences and the breadth of the emerging patterns of OVAW in Bangladesh, as provided for in Creswell and Plano Clark (2018). In this design, the qualitative phase precedes and informs the quantitative phase in the process of concept generation and instrument development that is clearly grounded in context-specific realities. Phase 1 involved semi-structured interviews with young women who had personally experienced online violence, in order to probe into forms, language, and perceived consequences of OVAW, and to identify salient cultural mechanisms, such as honor-based stigma and reputational harm, that shape these experiences. Themes extracted in this phase directly informed the structure, wording, and item categories of the subsequent survey questionnaire, ensuring cultural and linguistic relevance. In Phase 2, a larger population of women took part in a cross-sectional online survey assessing the frequency of the identified patterns, and variation by selected social and demographic factors. Integration across the two phases occurred at two levels: During the instrument development process, through which qualitative codes and illustrative narratives were transformed into measurable variables such as impersonation, hate speech, and withdrawal from public engagement. During interpretation, where qualitative insights helped explain and contextualize important statistical associations, for example when interviews revealed that it was community gossip and family honor which drove the impersonation experiences of rural women, thus explaining quantitative disparities across rural-urban respondents. This sequential linking ensured that the qualitative exploration was grounded in quantitative measurement, while statistical analyses served to widen the frames within which qualitative narratives could be interpreted. Sampling and Participants Purposive sampling was used to find people who had personally experienced online harassment or abuse. Since women who experience digital abuse are a hidden and stigmatized population that is challenging to reach through random or chance techniques, the purposeful recruitment was acceptable (Etikan et al., 2016; Palinkas et al., 2015). Social media groups, professional associations, and academic networks were used to recruit participants, and invitations to participate in the study were extensively disseminated with guarantees of secrecy. Between July and August of 2024, a total of 204 replies were gathered via Google Forms; after cleaning and quality check, 202 responses remained. Students and working women from both rural and urban locations were included in the sample. Online purposive sampling naturally restricts external validity, even as it made it possible to reach scattered respondents at a lower cost and with more anonymity than would be achievable in person for delicate subjects. Therefore, rather than being statistically representative of all Bangladeshi women, these results should be seen as reflecting young, digitally active women who willingly volunteered their experiences. Data Collection Procedures Phase 1: Qualitative Interviews. Semi-structured interviews were carried out in confidential online sessions. Participants were asked about the types of online abuse they experienced, perceived motivations of perpetrators, coping strategies, and emotional or social consequences. The interviews were recorded with consent and then transcribed verbatim for thematic analysis. Phase 2: Quantitative Survey. The survey instrument was developed from Phase 1 codes, structured into four sections: a) demographic characteristics; b) experiences of specific OVAW types, including impersonation, hate speech, and sextortion; c) psychological, behavioral, and physical impacts; d) and contextual factors such as intensity of internet use, occupation, and residence. Both nominal and ordinal scales were used to measure the items. The analysis made use of both descriptive and inferential statistics. SPSS v29 was used to examine quantitative data. Chi-square tests evaluated correlations between demographic data, while descriptive statistics detailed prevalence and consequence patterns. Standardized residuals were used to identify the categories that drove significant relationships (Sharpe, 2015). In order to transition from the original open coding to axial themes related to strain, stigma, and intersectional inequality, thematic analysis was carried out in accordance with Braun and Clarke (2019). To develop a comprehensive knowledge of the mechanisms of OVAW in the Bangladeshi sociocultural context, the results from both strands were interpreted together. Ethical Considerations: Consenting was secured electronically from all participants after providing information on research objectives, voluntary participation, and the right to withdraw at any stage. Identities and responses were anonymised, and all questions relating to personal experiences were optional to minimize distress. Contact information for hotlines for mental health support was also provided to participants at the end of every session. Limitations The results, therefore, are indicative rather than generalizable to the whole population of Bangladeshi women, given the purposive and online self-selection approach. Self-reporting and cross-sectional design further restrict causal inference. Future studies should consider stratified or probability sampling, offline inclusion strategies, and longitudinal designs to enhance representativeness and temporal validity. Findings Most prevalence of Online Violence The study used descriptive statistics to identify the prevalence of types of violence that have predominantly occurred online. Table 1 shows that descriptive statistics on the prevalence of experiences of online violence among young women and girls (N=202). The most common type of online violence found was impersonation (n = 78), with a mean score of .39 (38.6%) and a standard deviation of .488. The second and third most common online violence were hate speech (n=56), with a mean of. 28 and a standard deviation of .449, and offensive comments (n=34) with a standard deviation of .375. There were fewer reports of stalking (n=14), indicating 6.9% with a standard deviation of. 255, doxing and sextortion were 4% for each type, which indicates that these forms are relatively uncommon. Moreover, impersonation and hate speech are significantly more common than doxing and sextortion among young women and girls who have experienced some form of online violence. To test the significant differences in experiencing online VAW based on internet use, urbanity/rurality and occupations, this study used the Chi-square test of association. The chi-square tests in table 2 showed how the experience and incidence of online violence types were significantly different based on internet usage, location (urban vs rural), and occupation (students vs employed). Internet usage had shown significant associations with frequencies of experiencing violence (X²= 49.439, p =<.001), offensive comments (X²= 10.023, p =. 007), impersonation (X²= 24.247, p <.001), hate speech (X² = 27.935, p <. 001), and stalking (X²= 10.654, p =. 005). However, the relationships are significant in doxing(X²= 3.014, p = .222). This result indicates that there were significant differences in the experience of violence based on internet usage. The post hoc tests were performed to determine how frequencies of internet usage were related to violence types employing Standardized Residual greater than 1.96. The results show low and high internet users (standardized residual = 2.3 and 3.9) were associated with low and high frequencies of violence incidence. Moderate internet usage was related to moderate frequencies of violence incidence (standardized residual = 2.6). Moreover, low internet usage was also associated with offensive comment (standardized residual = 2.4) and stalking (standardized residual = 2.9). While moderate internet usage was associated with hate speech (standardized residual = 3.00), higher internet usage was associated with impersonation(standardized residual = 2.9 ). This study also tested how online violence was related to locations, whether young women live in rural and urban areas. The result found that impersonation (X²= 25.321, p <.001), doxing (X²= 7.695, p =. 006), and hate speech (X²=9.805, p <.002) were found significant. However, no significant relationships were found for frequencies of experiencing violence, (X²=3.506, p =.173), offensive comments (X² = 0.986, p =. 321), sextortion (X²= 3.689, p =. 055), and stalking (X²= 0.032, p =. 858). Post hoc tests showed impersonation (standardized residual = 3.3) and hate speech (standardized residual = 2.2) were higher in rural areas than urban areas. For occupations whether young women are currently student or employed, the result showed that relationship of frequencies of violence (X²= 65.197, p <. 001), offensive comment (violence (X²= 12.633, p <. 002), impersonation (X²= 33.601, p <. 001), and hate speech (X²= 83.296, p <. 001) were significant. Doxing, sextortion and stalking were not significant relationships with occupations. Post hoc test showed while the incidence of violence, offensive comment and impersonation among student and young employed women were the same, employed women have moderate frequencies of violence than student (standardized residual =3.3). In addition, hate speech is higher in employed women than students (standardized residual=6.8). Consequences of online violence The descriptive statistics also show the most prevalent consequences of online violence against young women. Table 3 provides descriptive statistics about the most common forms of online violence perpetrated against young women. The most common consequence of online violence was avoidance of participating in public spaces (n=126) with a mean score of 62 and a standard deviation of .486. Mental depression was reported 61.4% of young women with a standard deviation of .48807, which was followed by behavioral issues (31.7%) with them with a standard deviation of 1.075 and declining trust on others (20.8%) with a standard deviation of .40683. Finally, 11.9% of young women also mentioned about withdrew from online activities with a standard deviation of. 32437 and 5.9% indicated physical-related stress with a standard deviation of .23697. Consequences of online violence internet use, urbanity/rurality and occupations The researcher also tested if there were any significant differences in effects on health based on internet use, urbanity/rurality and occupations (Chi-square test of association). The test results also showed that the degree of health effects from online violence varies significantly depending on internet usage, rural or urban, and student or employed (table 4). Trust decline on others had strong associations with internet usage, X²= 31.839, p <.002; physical related stress, X²= 10.932, p =. 004; mental depression, X² = 58.678, p <. 001; withdrawal from online, X² = 16.021, p <.001; and behavior, X² = 26.422, p <.001. No significant association was found for avoidance of public space participation, X²= 1.158, p = .560 suggesting this consequence was less correlated with internet usage. The results of post hoc tests showed that trust erosion in other people was associated with low internet usage (standardized residual=2.6) and higher internet usage (standardized residual=2.6). While physical related stress (standardized residual=2.5), and withdrawal from online (standardized residuals=2.9) were associated with high internet usage, the mental depression was associated with moderate and high internet usages (standardized residuals=3.3 and 3.5). In terms of locations, this study found significant associations for trust decreased, X²=7.141, p <.008; physical related stress, X²= 16.619, p < .001; mental depression, X²= 47.776, p < .001; withdrawal from the internet, X²= 16.569, p <.001; and behavioral problems, X² = 15.698, p =.001. 0.001, indicating that urban residents are more likely to face these health-related consequences than rural residents. However, there was no significant association for avoidance of participation in public space, X²= 2.166, p =. 141. The post hoc tests also showed that decreased trust on others (standardized residual=2.00), physical related stress (standardized residual=3.30), withdrawal from online (standardized residual=3.20), and moderate behavioral problems (standardized residual=2.30) were higher in rural areas than urban areas. However, mental depression (standardized residual=2.40) was found higher in urban areas than rural areas. Based on occupational status, there were statistically significant differences in trust decreased significantly, X² = 17.442, p <. 001; mental depression, X²= 26.329, p <. 001; and behavioral, X²= 24.424, p <. 001. However, there were no significant associations for physical-related stress, X²= 0.507, p =.776; withdrawal from online, X²= 3.974, p =. 137, or avoidance of accessing public space, X² = 5.767, p =. 056. The post hoc tests showed mental depression (standardized residual=2.70) was higher in employed women than students. Consequences of violence across various types of online violence This study also performed a Chi-square test of association to demonstrate how the consequences of violence differ across various types of online violence. Table 5 showed the association between different forms of online violence against young women and mental, behavioral and physical consequences. Offensive comments were significant relationships with behavioral issues (X² =13.048, p =. 005) and avoidance of participating in public places (X² =6.952, p =. 008). In addition, impersonation was associated with a decline in trust in others (X² = 4.240, p =. 039), physical-related stress (X² = 4.236, p =. 040), mental depression (X² = 22.222, p =<.001), withdrawal from online (X² = 9.042, p =003), behavioral issues (X² = 26.485, p =<.001) and avoidance of public involvement (X² = 6.664, p = .010). Furthermore, hate speech decreased in trust (X² =4.77, p =.029), mental depression (X² =9.65, p =.002), and behavioral issues (X² =13.368, p =.004). However, doxing and stalking were not found to have a statistically significant association with any kind of online offense. Discussion Online Violence Against Women (OVAW) in Bangladesh is not simply a product of being exposed to the Internet. While quantitative data indicates that rural and lower frequency users experience more impersonation and hate speech than urban and/or higher frequency users, qualitative data reveals that the honor-based stigma which exists in Bangladesh, transforms any false information, regardless of whether it is an impersonation or a fabrication of any kind, into permanent damage to one's reputation and/or credibility. In addition, the limited digital literacy skills that exist for women in Bangladesh, make it difficult for women to detect, let alone respond to, fake profiles. Using the General Strain Theory (GST)–Intersectional Feminist (IF) lens as a framework, impersonation can be seen as a culturally specific form of strain that directly jeopardizes a woman's "honor," which is one of the most important social currencies for women. However, when considering intersectional disadvantages, such as living in rural areas, having a lower level of education, working outside the home, etc., these same disadvantages also limit access to coping resources, thereby increasing both the prevalence and harm of OVAW (Agnew, 1992 ; Crenshaw, 1991 ; Henry & Powell, 2018 ). Although the dominant forms of OVAW reported globally are impersonations and hate speech (Citron, 2014 ; Dragiewicz et al., 2018 ; Ging & Siapera, 2018 ); these types of harassment have unique meanings in Bangladesh. For example, the ease with which anonymous accounts can be created on-line and the lack of consistent enforcement of the Digital Security Act (DSA), makes impersonation easier to occur (Amnesty International, 2019 ; Rahman, 2021 ). Hate speech in Bangladesh, is similar to other forms of hate speech globally; however, hate speech in Bangladesh, often reflects moral policing and existing gendered hierarchies found in Bangladesh (Sultana & Islam, 2022 ). As expected, working women and rural women were significantly more likely to report experiencing higher levels of OVAW, as they have been identified as experiencing the greatest backlash from feminists when women deviate from their assigned traditional roles (Jane, 2017 ; Lewis et al., 2017 ). In addition, the unanticipated vulnerability of low frequency users, further emphasizes how the gender digital divide operates as a structural risk factor for OVAW (Antonio & Tuffley, 2014 ; Fatehkia et al., 2018 ). The psychological and social impacts of OVAW in Bangladesh, align with international findings; however, they indicate sharp silencing effects: High levels of depression, anxiety, and distrust, caused approximately 62% of OVAW victims to avoid using public space (Reed et al., 2019 ; Suzor et al., 2019 ; Vickery & Everbach, 2018 ). Although the avoidance of public spaces due to fear of encountering someone who will harass them on line has been documented internationally; the extent to which this occurs in Bangladesh, is far greater than has been documented in Western countries, demonstrating how the combination of digital victimization and patriarchal expectations to stay out of the public eye, results in the erosion of women's civic participation and digital citizenship (Baer, 2016 ; Mendes et al., 2018 ). Despite the many contributions made by this study, there are several limitations. First, the cross-sectional design of the study, limits the ability to determine causality between the variables studied. Second, the sample of the study was primarily composed of students from universities in Bangladesh and snowballing methods used to recruit participants, may have resulted in an underrepresentation of older women, women who are less educated, and women who do not use the internet. Third, the self-report measures of OVAW, may have been subject to recall bias and/or social-desirability bias, especially regarding sensitive topics related to honor. Fourth, the relatively small size of the qualitative sample (n = 28) limits the degree to which findings can be generalized across all regions of Bangladesh. Therefore, future research should use longitudinal study designs, draw samples from larger populations, and include non-internet using women to fully examine the spectrum of risk for OVAW in Bangladesh. In conclusion, OVAW in Bangladesh represents a culturally embedded form of patriarchal control over women, based upon honor-based social norms, digital inequality, and intersectional disadvantage. The findings of this study contribute to our understanding of the global phenomenon of technology facilitated gender-based violence in Low- and Middle-Income Countries (LMICs), and emphasize the necessity for developing contextually relevant and systemic interventions that address both the digital harms that result from OVAW and the offline systems that enable these harms to continue. Policy Recommendation Young women in Bangladesh experiencing online violence deserve immediate attention and coordination. Laws regarding cybercrime exist however there is no strong enforcement mechanism; therefore police units with digital forensic expertise must be created along with anonymous, confidential reporting mechanisms for victims to prevent stigma. The same applies to incorporating digital safety into school curriculum as well as using basic community training in remote areas where less frequent Internet users have a greater risk of being targeted. All educational institutions and workplaces must create and enforce clearly defined anti-harassment policy that provide support (grievance procedures, peer support groups, etc.) and that include local leaders who speak out. Accessible mental health services (hotlines, NGO counseling, etc.) must also be provided to assist survivors to both cope with their experience and continue participating in online activities. Overall, all sectors (government, technology companies, educational institutions, civil society) must work together to develop a national database of all reported incidents and address the underlying societal norms of patriarchy and "honor" based social structures that silence women. Only when all parties involved (enforcement agencies, educators, technology companies, civil society, etc.) make an effort to promote awareness (education), support (access to resources), and cultural change will young Bangladeshi women be able to fully engage and contribute equally to digital activities. Conclusion As an increasing number of people rely on digital technology to meet their most fundamental requirements, get information, and build social connections, it is also important to note that the digital environment represents new threats to both the safety of individuals, and their human rights. In Bangladesh, for example, research has shown that the technology-enabled Gender-Based Violence (GBV) is a widespread, gendered phenomenon which includes various forms of assault, abuse and harassment. Online Violence Against Women (OVAW), which encompasses cyber-bullying, sexual harassment, image-based abuse, threat messaging, and gendertrolling, predominantly impacts women who are visible in the public sphere. Further research is needed to fully understand the prevalence, manifestation, and impact of OVAW in Bangladesh due to the significant increase in prevalence over the last few years. Collaborative efforts are necessary to address the OVAW issue among all stakeholders, such as governments, law enforcement officials, NGOs/Civil Society Organizations, ICT companies, and researchers. If these actors share a common, yet context-specifically nuanced, understanding of this complex problem; then they may collaborate effectively to prevent, reduce, and respond to the Technology Facilitated GBV in a timely and trauma-informed manner. Declarations Funding statement: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Declaration of conflicting interests: The Author declares that there is no financial and/or non-financial conflict of interest. AI Statement: The author did not use AI tools during preparation of the manuscript. As the data was collected anonymously, so we didn’t take any ethical approval from any institution. Consenting was secured electronically from all participants after providing information on research objectives and all the respondents are taken anonymously. The article is not under consideration for publication elsewhere. This article received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The Authors declare that there is no conflict of interest. The author does not use generative AI in preparing this manuscript. The author declares that there are no hazards and human or animal subjects involved. The authors have written entirely original works and cited other works appropriately. All participants provided informed consent prior to participation. Clinical trial number: not applicable. There’s no competing interest. Consent to participate Freely given, informed consent to participate in the study was obtained from all participants prior to data collection. Participants were informed about the purpose of the study, the voluntary nature of participation, potential risks and benefits, and their right to withdraw at any time without consequence. Ethics Statement This study involved the collection of anonymous survey data from human participants and was conducted in accordance with the ethical principles of the Declaration of Helsinki. Participation was voluntary, and no personally identifiable information was collected. The requirement of formal ethical approval for this study was waived by the Departmental Research Ethics Committee, Department of Public Administration, University of Chittagong, in accordance with national guidelines and the Declaration of Helsinki, due to the minimal-risk nature of the study and the use of fully anonymized data. Consent to publish Informed consent for publication of anonymized data was obtained from all participants as part of the informed consent process before participation. Data Availability The datasets generated during and/or analyzed during the current study are not publicly available due to the sensitive nature of the data involving experiences of online violence and mental health, which could potentially compromise participant anonymity and privacy, but are available from the corresponding author on reasonable request. References Alotaibi, N. B. (2019). Cyber bullying and the expected consequences on the students’ academic achievement. IEEE access, 7, 153417-153431. Agnew, R. (1992). Foundation for a general strain theory of crime and delinquency. Criminology, 30(1), 47–87. https://doi.org/10.1111/j.1745-9125.1992.tb01093.x Agnew, R. (2006). Pressured into crime: An overview of general strain theory. Oxford University Press. Ahmed, S., Kabir, A., Sharmin, S., & Jafrin, S. (2017). Cyber-crimes against womenfolk on social networks: Bangladesh context. International Journal of Computer Applications, 174(4), 9–15. https://doi.org/10.5120/ijca2017915407 Amin, R. (2024). Causes and consequences of cyberbullying against women in Bangladesh: A comprehensive study. SALASIKA: Indonesian Journal of Gender, Women, Child, and Social Inclusion Studies, 6(2), 127–145. https://doi.org/10.36625/sj.v6i2.123 Amnesty International. (2019). Bangladesh: Surveillance and repression under the Digital Security Act . https://www.amnesty.org/en/documents/asa13/1422/2019/en/ Antonio, A., & Tuffley, D. (2014). The gender digital divide in developing countries. Future Internet , 6(4), 673–687. https://doi.org/10.3390/fi6040673 Baer, H. (2016). Redefining rape culture: Online feminist activism against sexual violence. Feminist Media Studies , 16(4), 634–650. https://doi.org/10.1080/14680777.2016.1138234 Bangladesh Telecommunication Regulatory Commission. (2023). Internet subscriber statistics in Bangladesh. https://www.btrc.gov.bd/site/page/0ae3ff81-68d7-4dd6-8b52-9b1c26102480/- Bansal, V., Rezwan, M., Iyer, M., Leasure, E., Roth, C., Pal, P., & Hinson, L. (2024). A scoping review of technology-facilitated gender-based violence in low- and middle-income countries across Asia. Trauma, Violence, & Abuse , 25(1), 463–475. https://doi.org/10.1177/15248380231154614 Bansal, N. (2024). Online harassment and violence against women in Asia-Pacific: A scoping review. Trauma, Violence, & Abuse, 25(3), 678–695. https://doi.org/10.1177/15248380231154614 Barker, k., & Jurasz,O. (2020). Online violence against women as an obstacle to gender equality: a critical view from Europe. European Equality Law Review, 2020 (1), 47-60. Blackwell, L., Lo, K., & Marwick, A. E. (2023). How platforms moderate online abuse: Research perspectives and policy implications. Social Media + Society, 9(1). https://doi.org/10.1177/20563051231157297 Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806 Braekman, E., Vandenheede, H., Charafeddine, R., & Demarest, S. (2022). Comparing web-based and face-to-face survey modes in health research: Cost, ease, and representativeness. Journal of Medical Internet Research, 24(1), e30366. https://doi.org/10.2196/30366 Caridade, S. M. M., Dinis, M. A., & Pereira, V. E. (2022). The predictive role of psychological vulnerability and sense of security in cybervictimization in adolescents. Journal of Interpersonal Violence , 37(23–24), NP22731–NP22756. https://doi.org/10.1177/08862605211063011 Celuch, M., Latikka, R., Oksa, R., Savela, N., & Oksanen, A. (2023). Self-censorship among online harassment targets: The role of support at work, harassment characteristics, and the target’s public visibility. Information, Communication & Society . Advance online publication. https://doi.org/10.1080/1369118X.2023.2289978 Citron, D. K. (2014). Hate crimes in cyberspace . Harvard University Press. https://doi.org/10.4159/harvard.9780674735616 Crenshaw, K. (1989). Demarginalizing the intersection of race and sex: A Black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum, 1989(1), 139–167. Crenshaw, K. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review , 43(6), 1241–1299. https://doi.org/10.2307/1229039 Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage Publications. Davidson, J., Suzor, N., Dragiewicz, M., Harris, B., Gillett, R., Burgess, J., & Van Geelen, T. (2019). Human rights by design: The responsibilities of social media platforms to address gender-based violence online. Policy & Internet, 11(1), 84–103. https://doi.org/10.1002/poi3.185 Academia Douglas, D. M., McGarty, C., & Lyddy, F. (2019). The psychology of online harassment. Journal of Social Issues, 75(1), 1–16. https://doi.org/10.1111/josi.12303 Dragiewicz, M., Burgess, J., Matamoros-Fernández, A., Salter, M., Suzor, N. P., Woodlock, D., & Harris, B. (2018). Technology facilitated coercive control: Domestic violence and the competing roles of digital media platforms. Feminist Media Studies , 18(4), 609–625. https://doi.org/10.1080/14680777.2018.1447346 Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11 European Institute for Gender Equality. (2017). Cyber violence against women and girls – Key terms and concepts. https://eige.europa.eu/publications/cyber-violence-against-women-and-girls-key-terms-and-concepts Fardouly, J., Magson, N. R., Rapee, R. M., Johnco, C. J., & Oar, E. L. (2023). The impact of social media use on appearance self-esteem from early to late adolescence: An eye-tracking study. Body Image , 44, 161–170. https://doi.org/10.1016/j.bodyim.2022.12.007 Fatehkia, M., Kashyap, R., & Weber, I. (2018). Using Facebook ad data to track the global digital gender gap. World Development , 107, 189–200. https://doi.org/10.1016/j.worlddev.2018.03.007 Felten, J. (2023). Violence against women and girls as a public health crisis during humanitarian emergencies. Journal of International Humanitarian Action, 8(1), 1–10. https://doi.org/10.1186/s41018-023-00142-5 Gámez-Guadix, M., Mateos-Pérez, E., & Cuervo-Ortega, S. (2023). Technology-facilitated sexual violence victimization: Prevalence, risk factors, and relationship with offline sexual violence among adolescents. Computers in Human Behavior, 141, Article 107610. https://doi.org/10.1016/j.chb.2022.107610 Gámez-Guadix, M., Borrajo, E., & Calvete, E. (2019). Cybervictimization and mental health in adolescents: A systematic review. Aggression and Violent Behavior , 49, 101326. https://doi.org/10.1016/j.avb.2019.101326 Ging, D., & Siapera, E. (2018). Special issue on online misogyny. Feminist Media Studies , 18(4), 515–524. https://doi.org/10.1080/14680777.2018.1483379 Global Fund for Women. (2015). Report on cross-cultural program and policy design to address online violence against women. Global Fund for Women. Retrieved from https://www.globalfundforwomen.org Hegarty, K., Tarzia, L., & Valpied, J. (2021). Depression and post-traumatic stress disorder in women exposed to intimate partner violence: A longitudinal study. Australian & New Zealand Journal of Psychiatry , 55(5), 497–507. https://doi.org/10.1177/0004867420963745 Henry, N., Flynn, A., & Powell, A. (2024). Technology-facilitated sexual violence: A review of the evidence and policy responses. Trends in Organized Crime, 27(1), 1–22. https://doi.org/10.1007/s12117-023-09512-5 Henry, N., & Powell, A. (2015). Embodied harms: Gender, shame, and technology-facilitated sexual violence. Violence Against Women, 21(6), 758–779. https://doi.org/10.1177/1077801215576581 Henry, N., & Powell, A. (2018). Technology-facilitated sexual violence: A literature review of empirical research. Trauma, Violence, & Abuse, 19(2), 195–208. https://doi.org/10.1177/1524838016650189 Henry, N., Flynn, A., & Powell, A. (2023). Technology-facilitated sexual violence: A multidisciplinary approach to developing literature and informing practice. Trauma, Violence, & Abuse , 24(2), 313–323. https://doi.org/10.1177/15248380211043296 Hellevik, S. (2025). Gendered online harassment of politicians and journalists: A global systematic review. Journal of Communication, 75(1), 34–56. https://doi.org/10.1093/joc/jqab021 Hinduja, S. (2025). Cyberbullying through the lens of trauma: Exploring the role of exclusion, rejection, and PTSD symptoms. BMC Public Health, 25(1), Article 22692. https://doi.org/10.1186/s12889-025-22692-6 Islam, R., & Rahman, M. (2023). Cultural norms and the silencing of violence against women in Bangladesh. Asian Journal of Women’s Studies, 29(1), 77–95. https://doi.org/10.1080/12259276.2023.2173681 Iroegbu, M. (2024). Investigating the psychological impact of cyber-sexual harassment (CSH) in adult women. International Journal of Environmental Research and Public Health, 21(4), 112837. https://doi.org/10.3390/ijerph21040123 Jane, E. A. (2017). Misogyny online: A short (and brutish) history . SAGE Publications. https://doi.org/10.4135/9781473916029 Kamal, S. B. (2024). Women victimization in social networking sites in Bangladesh. International Journal of Innovative Science and Research Technology, 9(11), 1047–1052. https://doi.org/10.38124/ijisrt/ijisrt24nov447 Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140(4), 1073–1137. https://doi.org/10.1037/a0035618 Kuper, H., & Wachter, K. (2023). Online violence against women journalists: A global snapshot. Journalism Practice , 17(4), 735–754. https://doi.org/10.1080/17512786.2022.2084882 Lewis, R., Rowe, M., & Wiper, C. (2024). Technology-facilitated gender-based violence against politically active women: A systematic review of psychological and political consequences and women’s coping behaviors. Trauma, Violence, & Abuse . Advance online publication. https://doi.org/10.1177/15248380231189000 Lewis, R., Sharp, E., Remnant, J., & Redpath, R. (2017). ‘Safe spaces’: Experiences of feminist women-only space. Sociological Research Online , 20(4), 105–118. https://doi.org/10.5153/sro.3915 Malanga, W. A. (2021). Cyber violence against women in rural Malawi: Forms, motivations, and coping strategies. arXiv preprint. https://arxiv.org/abs/2108.09806 Marwick, A. E., Blackwell, L., & Lo, K. (2021). “Yet another Twitter sh*tstorm”: Contentious communication and the normative dynamics of social media crowds. American Behavioral Scientist, 65(3), 321–345. https://doi.org/10.1177/0002764221989781 Mendes, K., Ringrose, J., & Keller, J. (2018). #MeToo and the promise and pitfalls of challenging rape culture through digital feminist activism. European Journal of Women’s Studies , 25(2), 236–246. https://doi.org/10.1177/1350506818765318 Mitchell, K. J. (2025). Measuring technology-facilitated violence: Methodological challenges and directions for research. Violence Against Women, 31(2), 145–167. https://doi.org/10.1177/10778012231123456 Monni, S. S., & Sultana, A. (2016). Investigating cyber bullying: Pervasiveness, causes and socio-psychological impact on adolescent girls. Journal of Public Administration and Governance, 6(4), 12–24. https://doi.org/10.5296/jpag.v6i4.10132 Mridha, M. R., Ashrafuzzaman, S. M., & Sara, S. S. (2024). Uncovering the prevalence and consequences of cyberbullying among female students as virtual violence in Bangladesh. International Journal of Social Science Research and Review, 7(7), 46–57. https://doi.org/10.47814/ijssrr.v7i7.2167 Mukred, M. (2024). The roots of digital aggression: Exploring cyber-violence against women. Online Safety Journal, 10(2), 45–59. https://doi.org/10.1016/j.safe.2024.05.001 Ornstein, M. D., McLeod, C. B., & Smith, P. M. (2023). The psychosocial impacts of intimate partner violence against women in LMIC contexts: Toward a holistic approach. Trauma, Violence, & Abuse , 24(5), 1315–1329. https://doi.org/10.1177/15248380221103252 Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544. https://doi.org/10.1007/s10488-013-0528-y Paradiso, M., Santoro, G., & Di Marco, S. (2024). Online misogyny and its consequences: A systematic review. Aggression and Violent Behavior, 75, 101945. https://doi.org/10.1016/j.avb.2023.101945 Polyzoidou, V. (2024). Digital violence against women: Is there a real need for discussion? International Journal for the Semiotics of Law, 37, 123–142. https://doi.org/10.1007/s11196-024-10179-3 Posetti, J., Shabbir, N., Maynard, D., Bontcheva, K., & Aboulez, N. (2022). The chilling: A global study of online violence against women journalists. International Journal of Press/Politics , 27(4), 927–954. https://doi.org/10.1177/19401612221108144 Powell, A., & Henry, N. (2017). Sexual violence in a digital age. Palgrave Macmillan. Rahman, M. M. (2021). The Digital Security Act 2018 in Bangladesh: An assessment of its impact on freedom of expression. Asian Journal of Comparative Law , 16(S1), S57–S70. https://doi.org/10.1017/asjcl.2021.13 Rahman, A., & Khan, N. (2021). Digital misogyny in Bangladesh: Exploring the intersection of culture and technology. Journal of South Asian Media Studies, 15(3), 201–218. https://doi.org/10.1080/17513057.2021.1990412 Regmi, P. R., Waithaka, E., Paudyal, A., Simkhada, P., & Van Teijlingen, E. (2016). Guide to the design and application of online questionnaires. Journal of Clinical and Diagnostic Research, 10(6), ZC01–ZC04. https://doi.org/10.7860/JCDR/2016/17824.7901 Reed, L. A., Tolman, R. M., & Ward, L. M. (2019). Gender matters: Experiences of cyber sexual harassment among adolescent girls. Journal of Adolescent Research , 34(5), 533–559. https://doi.org/10.1177/0743558418792478 Reed, L. A., Tolman, R. M., & Ward, L. M. (2022). Cyber sexual harassment and psychological distress among young adult women. Journal of Interpersonal Violence , 37(15–16), NP13345–NP13368. https://doi.org/10.1177/08862605211015291 Salem, M. R., Albaghli, H., & Alasmary, W. (2023). Anonymity and response validity in online surveys: Evidence from sensitive-topic research. BMC Public Health, 23(1), 1456. https://doi.org/10.1186/s12889-023-14567-3 Sardinha, L., Maheu-Giroux, M., Stöckl, H., Meyer, S. R., & García-Moreno, C. (2022). Global, regional, and national prevalence estimates of physical or sexual, or both, intimate partner violence against women in 2018. The Lancet , 399(10327), 803–813. https://doi.org/10.1016/S0140-6736(21)02664-7 Sharpe, D. (2015). Chi-square test is statistically significant: Now what? Practical Assessment, Research, and Evaluation, 20(1), 8. https://doi.org/10.7275/tbfa-x148 Sheikh, M. M. R., & Rogers, M. M. (2023). Technology-facilitated sexual violence and abuse in low and middle-income countries: A scoping review. Trauma, Violence, & Abuse, 25(2), 1614–1629. https://doi.org/10.1177/15248380231191189 Sheikh, M. M. R., Hossan, M. R., & Menih, H. (2023). Cyberbullying victimization and perpetration among university students in Bangladesh: Prevalence, impact and help-seeking practices. Journal of School Violence, 22(2), 198–214. https://doi.org/10.1080/15388220.2023.2168681 Stöckl, H. (2024). Violence against women as a public health issue. Annual Review of Public Health, 45(1), 341–360. https://doi.org/10.1146/annurev-publhealth-060722-025138 Sultana, A., & Islam, N. (2022). Online gender-based violence in Bangladesh: A study on university students. Journal of Social Studies , 158, 45–68. Suzor, N. P., Dragiewicz, M., Harris, B., Gillett, R., Burgess, J., & Van Geelen, T. (2019). Human rights by design: The responsibilities of social media platforms to address gender-based violence online. Policy & Internet , 11(1), 84–103. https://doi.org/10.1002/poi3.185 Udo, G. G., & Bagchi, K. K. (2020). USING PERSONAL NORM MODEL TO EXPLAIN CYBER-HARASSMENT INTENTION AND BEHAVIOR. Issues in Information Systems, 21(4). Ullman, S. E., et al. (2025). Global interventions to prevent violence against women and girls: A systematic review of the RESPECT Women framework. The Lancet Global Health, 13(2), e152–e165. https://doi.org/10.1016/S2214-109X(24)00556-4 United Nations Regional Office for Arab States & UN Women. (2021). Online violence against women in the Arab States region. United Nations. https://pmc.ncbi.nlm.nih.gov/articles/PMC10913330/ United Nations Regional Information Centre. (2024, January 18). Cyberviolence against women and girls: The growing threat of the digital age. United Nations. https://unric.org/en/cyberviolence-against-women-and-girls-the-growing-threat-of-the-digital-age/ UN Women. (2021). Online and ICT-facilitated violence against women and girls during COVID-19. United Nations. https://www.unwomen.org/en/digital-library/publications/2021/07/online-and-ict-facilitated-violence-against-women-and-girls-during-covid-19 UN Women. (2023). Facts and figures: Ending violence against women . https://www.unwomen.org/en/what-we-do/ending-violence-against-women/facts-and-figures Vandenbosch, L., & van Oosten, J. M. F. (2017). The relationship between online victimization and psychological outcomes: A meta-analysis. Journal of Youth and Adolescence, 46(6), 1343–1361. https://doi.org/10.1007/s10964-017-0672-x Vickery, J. R., & Everbach, T. (Eds.). (2018). Mediating misogyny: Gender, technology, and harassment . Palgrave Macmillan. https://doi.org/10.1007/978-3-319-72917-6 Wessells, M. G., & Kostelny, K. (2022). The Psychosocial Impacts of Intimate Partner Violence against Women in LMIC Contexts: Toward a Holistic Approach. International Journal of Environmental Research and Public Health, 19(21), 14488. https://doi.org/10.3390/ijerph192114488 Woodlock, D. (2023). Technology-facilitated stalking: Expanding the research and response landscape. Current Issues in Criminal Justice, 34(2), 135–152. https://doi.org/10.1080/10345329.2022.2159528 World Health Organization. (2021). Violence against women prevalence estimates, 2018: Global, regional and national prevalence estimates for intimate partner violence against women and global and regional prevalence estimates for non-partner sexual violence against women. WHO. https://www.who.int/publications/i/item/9789240022256 Worsley, J. D., McIntyre, J. C., Corcoran, R., & Bentall, R. P. (2023). Childhood maltreatment and problematic social media use: The mediating role of attachment and mental health. Computers in Human Behavior, 138, Article 107479. https://doi.org/10.1016/j.chb.2022.107479 Tables Table 1: Prevalence of Online Violence Against Young Women (n=202) Items Frequency % Mean Standard deviation Offensive Comment 34 16.8 .17 .375 Doxing 8 4 .04 .196 Impersonation 78 38.6 .39 .488 Hate Speech 56 27.7 .28 .449 Sextortion 8 4 .04 .196 Stalking 14 6.9 .07 .255 Table 2: Chi-Square Test of Association at Location, Internet Use and Occupations Internet usage Location Occupation Items X² P Rural Urban X² P Student Employed X² P Frequencies of violence 49.439 <.001 54 134 4.947 .026 140 46 65.197 <.001 Offensive Comment 10.023 .007 8 26 .986 .321 28 4 12.633a .002 Doxing 3.014 .222 0 8 7.695 .006 8 0 1.446 .485 Impersonation 24.247 <.001 40 38 25.321 <.001 76 2 33.601a <.001 Hate Speech 27.935 <.001 8 48 9.805 .002 18 38 83.296a <.001 Stalking 10.654 .005 4 10 .032 .858 12 2 .936a .626 Table 3: Most consequences of online violence (n=202) Items Fre % Mean Standard deviation Total Trust decreased on other 42 20.8 .2079 .40683 202 Physical Related Stress 12 5.9 .0594 .23697 202 Mental Depression 124 61.4 .6139 .48807 202 Withdrawal from online 24 11.9 .1188 .32437 202 Avoidance of Participating in public 126 62.4 .62 .486 202 Behavior 64 31.7 1.67 1.075 202 Table 4: differences in health consequences based on internet use, locations, and occupations Internet usage Locations Occupations Items X 2 P Rural Urban X 2 P Student Employed X 2 p Trust decreased 31.839 <.001 20 22 7.141 .008 0 42 17.442 <.001 Physical related Stress 10.932 .004 10 2 16.619 <.001 46 2 .507 .776 Mental_depression 58.678 <.001 16 108 47.776 <.001 44 78 26.329 <.001 Withdrawal from Online 16.021 <.001 16 8 16.569 <.001 2 22 3.974 .137 Behavior 26.422 <.001 62 140 15.698 .001 24 38 24.424 <.001 Avoidance of participation in public space 1.158 .560 34 92 2.166 141 36 88 5.767 .056 Table 5 : Table 5: Relations Between Online Violence Types and Their Effects on Young Women Items Offensive Comment Doxing Impersonation Hate Speech Sextortion Stalking X 2 P X 2 p X 2 p X 2 p X 2 P X 2 P Trust decreased .246 .620 .187 .666 4.240 .039 4.77 .029 .090 .765 .387 .534 Physical related Stress .00 .987 1.097 .295 4.236 .040 .778 .378 .526 .468 .950 .330 Mental depression 1.460 .227 .009 .924 22.222 <.001 9.654 .002 .651 .420 3.756 .053 Withdrawal from Online 1.405 .236 .006 .936 9.042 .003 1.662 .197 1.123 .289 2.028 .154 Behavioral issues 13.048 .005 5.231 .156 26.485 <.001 13.368 .004 9.326 .025 5.518 .138 Avoidance of participation in public space 6.952 .008 .992 .992 6.664 .010 .120 .729 .544 .461 .525 .469 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8364676","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582202811,"identity":"092a3575-af3b-4933-895b-5328edd72d97","order_by":0,"name":"Nowshin Islam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYJACZgYDBgY2BuYGBoYKmAgbUVoYgVrOEK0FDIBaGNuI0GLO3mP8uaDgDgMf/8HGTzfn2SU2sB9+wFxQhluLZc8ZM+kZBs8Y2CQSm6VztyUnNvCkGTDPOIdbi8GN3G3MPAaHgVoYG4BaDiQ2MOQwMPO24dWy+TNYC//B5t+5c4Ba+N8Q1LJBGqyFIbFNOrcBqEWCkC1nzn8DaeEB+qXNOudYsnGbxDODw3j9crwt+TPPn8Ny8v2HD9/OqbGT7edPfvgYX4jBAA+cBYqRA4Q1jIJRMApGwSjABwBKL0rCxC/IogAAAABJRU5ErkJggg==","orcid":"","institution":"University of Chittagong","correspondingAuthor":true,"prefix":"","firstName":"Nowshin","middleName":"","lastName":"Islam","suffix":""},{"id":582202812,"identity":"1588f85a-9fa6-4fda-b999-cf200b237397","order_by":1,"name":"Mahmudur Rahman Saidy","email":"","orcid":"","institution":"University of Chittagong","correspondingAuthor":false,"prefix":"","firstName":"Mahmudur","middleName":"Rahman","lastName":"Saidy","suffix":""},{"id":582202813,"identity":"3fdd880a-bfc8-4f80-90a8-266d4bcb4465","order_by":2,"name":"Nasir Uddin","email":"","orcid":"","institution":"University of Chittagong","correspondingAuthor":false,"prefix":"","firstName":"Nasir","middleName":"","lastName":"Uddin","suffix":""}],"badges":[],"createdAt":"2025-12-15 09:55:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8364676/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8364676/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103628553,"identity":"b5fab7e3-01a3-4f93-9d0e-9c5f2e9e2a29","added_by":"auto","created_at":"2026-02-27 21:39:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1104523,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8364676/v1/6d03ffcd-539e-41c9-9e09-dd65d82950b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Patterns of online impersonation violence and mental health consequences among women in Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe digital revolution has fundamentally transformed connectivity, creating new opportunities for women to attain education, enhance economic productivity, and engage in civic action (Bansal et al., 2024; Henry et al., 2024). Despite the revolution's many beneficial outcomes, the prevalence of violence against women (VAW) on the internet (OVAW), a kind of gender violence committed over digital communications networks, has increased (Gámez-Guadix et al., 2023; Woodlock, 2023). OVAW encompasses many violent behaviors such as stalking, hate speech, sexualized threats, impersonation, and non-consensual image sharing (Polyzoidou, 2024; Mukred, 2024; UNFPA, 2024). In the wider literature, this phenomenon is also referred to as \u003cem\u003etechnology-facilitated gender-based violence\u003c/em\u003e (TfGBV). Rooted in patriarchal power relations, OVAW exploits the anonymity and borderless reach of digital platforms to reproduce and amplify offline gender inequities, thereby posing serious risks to women’s psychological, behavioral, and physical well-being (Citron, 2014; Henry \u0026amp; Powell, 2018; UN Women, 2021). Increasingly, it is recognized not only as a neglected public health emergency but also as a significant impediment to achieving gender-equitable development (Stöckl, 2024; Felten, 2023).\u003c/p\u003e\n\u003cp\u003eBecause offenders frequently get away with it, online violence has spread throughout the world and has no boundaries. Between 16% and 58% of women and girls globally are thought to have personally experienced OVAW. OVAW is a systematic silencing of women, exclusion from digital venues, and disturbance of their agency; it cannot be dismissed as simple online wrongdoing. Beyond short-term discomfort, OVAW can have long-term negative effects such despair, anxiety, PTSD, and social disengagement. Additionally, empirical studies show that OVAW exacerbates gender disparities already present in offline sociocultural contexts and is linked to a decline in mental health.\u003c/p\u003e\n\u003cp\u003eThese international trends intersect in Bangladesh with nationally based socio-cultural forces wherein female \"honor\" norms and victim-blaming compound trauma, deter reporting, and enable perpetrator impunity. The combination of transnational online misogyny with patriarchal Bangladeshi institutions-grounded in religious conservatism and family honor codes-intensifies the incidence of harm undercutting female digital participation and mental well-being. However, studies of OVAW remain few in number, with dominant studies of OVAW being unipolar and focused on only select sections such as students alone in Bangladesh. For example, Mridha, Ashrafuzzaman, and Sara (2024) studied cyberbullying of girl students and documented significant social and mental effects. However, for larger demographic, social, and geographical settings than student groups, studies of differentiated risk of OVAW are sparse. Previous work has tended to focus on adolescents (Monni \u0026amp; Sultana, 2016) or considered cybercrime as a generic category (Ahmed et al., 2017). Such studies shed little light on how targeted types of OVAW—such as impersonation versus hate speech—generate different harms by occupations or regions (Mridha et al., 2024). Further, variables such as internet usage frequency, rural/urban residence, and employment status have been reported but remain little studied as risk determinants. Underreporting, fortified by culture- and stigma-enforced blame, persists in hiding the extent of harm (Islam \u0026amp; Rahman, 2023). Individually, these gaps reflect how OVAW not only threatens Bangladeshi women's well-being but also presents a broader challenge to Bangladesh's people-centered vision of inclusive, people-centered advancement.\u003c/p\u003e\n\u003cp\u003eThe current study tries to fill these gaps by adopting an exploratory sequential mixed-methods design, wherein the qualitative interviews were conducted first to identify the dominant categories of OVAW, and such insight informed the design of a quantitative survey. Throughout this paper, the terms TfGBV and OVAW are used interchangeably, but for clarity and consistency, the term OVAW has been used throughout. This study furthers theory by testing the boundary conditions of GST within a high-stigma, low enforcement LMIC; it demonstrated that impersonation acts as a ‘reputational strain’ magnified by honor norms, whereas Intersectional Feminism showed that rural/employed women are intersectional risk nodes-a finding that leaves many universal OVAW models challenged. The current study aims to map the most common forms of OVAW in Bangladesh; assess their impact on psychological, behavioral, and physical parameters; and analyze how these vary by employment status, geographic location, and internet use intensity. The current study aims to map the most common forms of OVAW in Bangladesh; assess their impact on psychological, behavioral, and physical parameters; and analyze how these vary by employment status, geographic location, and internet use intensity. Overall, it seeks to develop new empirical evidence that places OVAW in both national and global contexts, identifying it as a pressing human rights and development concern that needs to be addressed to advance gender-equitable digital citizenship and people-centered development in Bangladesh.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main objectives of this research are to\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eTo identify the predominant forms of online violence against women (OVAW) experienced by young women in Bangladesh;\u003c/li\u003e\n \u003cli\u003eTo assess the extent to which cultural stigma amplifies mental health impacts beyond global patterns;\u003c/li\u003e\n \u003cli\u003eTo analyze the influence of key socio-structural factors — intensity of internet use, rural/urban residence, and employment status — on the prevalence and severity of OVAW.\u003c/li\u003e\n\u003c/ul\u003e\u003cp\u003e"},{"header":"Literature Review and Theoretical Framework","content":"\u003cp\u003e\u003cstrong\u003e1. Conceptualising Online Violence Against Women (OVAW)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eConventionally, OVAW refers to gendered abuse, harassment, or exploitation enacted through digital technologies and platforms. Contributing scholars variously use a range of overlapping terms—such as technology-facilitated gender-based violence (TfGBV), cyber-VAWG, and technology-facilitated sexual violence (TFSV)—in an effort to capture different emphases, but all agree that online spaces mirror and often intensify patriarchal power structures (Henry \u0026amp; Powell, 2015, 2018). Research clearly demonstrates that such practices extend far beyond everyday \"incivility.\" They amount to forms of structural violence characterized by persistence, scalability, algorithmic amplification, and heightened visibility, allowing coercive control to permeate digital publics (Citron, 2014; Henry \u0026amp; Powell, 2018; Jane, 2017). More recently, studies of platform governance have similarly suggested that the moderation of content and the design of features are important for determining how OVAW is both experienced and problematized. Policy choices and technology affordances may either intervene helpfully or helplessly to heighten or diminish harm, highlighting rights-based, victim-oriented approaches to the development of safer internet environments (Blackwell, Lo, \u0026amp; Marwick, 2023; Suzor et al., 2019).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2. Types and Classifications of OVAW\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePrevious studies have investigated the causes and consequences of OVAW and compared the traditional violence against women with OVAW (Khan et al., 2023; Rahman \u0026amp; Hasan, 2018; Filice et al., 2022; Henry \u0026amp; Powell, 2015). Khan et al. (2023) argue that victims of OVAW may experience long-term effects that range from financial loss to mental or emotional stress and, in certain cases, trouble finding housing and employment. Rahman and Hasan (2018) reveal that there are five main reasons why emotional violence occurs in public, and they are patriarchy, family values, gendered socialization, societal standards, and morals. In digital spaces, causes of abuse include pornography addiction and easy access to platforms where offenders evade punishment (Filice et al., 2022). Therefore, the consecutive effect of OVAW has long-term impacts on victims’ mental health. Henry \u0026amp; Powell (2015) argue that both traditional and online violence are forms of gender-based violence, stemming from patriarchal structures that seek to dominate or harm women. Although these scholars examined the causes and consequences of OVAW in contexts such as the USA and Middle East, they overlooked the specific consequences of OVAW among young educated women in Bangladesh and failed to explore differences across urban–rural or occupational divides.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e3. Global Prevalence and Trends\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eWhile there has been an increased awareness of the issue of online violence against women (OVAW), estimating the true extent of the problem can be problematic. The lack of consistent definitions of OVAW, the variability of the ways that researchers conduct surveys, and the fact that many victims of OVAW do not report their experiences because they are fearful of further victimization and/or are afraid of being ostracized by society (Sardinha et al., 2022; Gámez-Guadix et al., 2019), all create barriers to understanding how widespread the phenomenon of OVAW is worldwide. Despite the problems associated with measuring the global prevalence of OVAW, numerous regional and international studies have reported high levels of exposure to various forms of online violence against women. Additionally, the literature indicates that the prevalence of online violence varies based on the platform used, the country of origin, the individual's age, and cultural context (Henry et al., 2023; Bansal et al., 2024).\u003c/p\u003e\u003cp\u003eA recent study of the prevalence of technology-facilitated gender-based violence among girls and women in low- and middle-income countries in Asia estimated that between 14% and 75% of girls and women have experienced some form of OVAW during their lives. In particular, girls and women who were between 15 and 25 years old at the time they first accessed an image-based platform were found to have higher levels of OVAW than other individuals (Bansal et al., 2024). There is growing research to indicate that women in public roles experience greater amounts of OVAW than others. According to a number of surveys, 20-73% of female politicians and journalists have experienced severe online abuse, which can result in self-censorship and a reduction in their ability to participate in the democratic process (Kuper \u0026amp; Wachter, 2023; Posetti et al., 2022). Younger women, women who identify as sexual and/or gender minorities, and women who hold public positions are among the most vulnerable to OVAW, while women in lower-middle-income countries are more likely to experience both higher levels of OVAW and more severe OVAW, due to a combination of cultural factors, such as stigma related to patriarchy, inadequate legal frameworks, insufficient responsibility of social media platforms for OVAW, and fragmented responses to OVAW from institutions (UN Women, 2023; Sheikh \u0026amp; Rogers, 2024).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e4. Psychological, Behavioral, and Health Consequences\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe majority of empirical research demonstrates a strong positive association between experiencing online violence and negative mental health outcomes. Specifically, research has demonstrated that girls and women who experience online violence are more likely to suffer from depression, anxiety, PTSD, and decreased self-esteem (Fardouly et al., 2023; Caridade et al., 2022). Many of the mental health consequences of OVAW are accompanied by corresponding behavioral changes, such as withdrawal from online communities, self-censorship, and avoidance of public engagement, which serve to exacerbate pre-existing gender inequality in areas such as education, employment, and politics (Celuch et al., 2023; Posetti et al., 2022). Research has also demonstrated that physical health consequences, including sleep disturbances caused by stress, chronic headache pain, and unexplained somatic symptoms, are common among women who experience online violence (Hegarty et al., 2021; Worsley et al., 2023). Furthermore, longitudinal and cross-sectional studies have established that repeated exposure to OVAW increases the likelihood of developing a traumatic response and leads to a heightened risk of long-term disengagement from digital publics (Reed et al., 2022; Lewis et al., 2024). Overall, the body of research on OVAW provides clear evidence that it constitutes a serious threat to the mental health and well-being of girls and women, and that it serves as a structural barrier to achieving gender equality in virtually every area of public and private life (UN Women, 2023; Ornstein et al., 2023).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e5. Scenario in Lower Middle Income Countries (LMICs) and Bangladesh\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIn LMICs, OVAW is compounded by patriarchal norms, stigma, and weak institutions. Early studies in Bangladesh focused on either adolescents or broad trends in cybercrime. More recently, studies looking at university students reported severe emotional, psychological, and social consequences. However, these studies have limitations: most relied on self-selective samples of students, used the “cyberbullying” umbrella concept, and rarely distinguished offense types. Few studies assessed the role of internet use intensity, urban–rural residence, and occupational status factors that are highly relevant to exposure and coping. The current study addresses such gaps by explicitly disaggregating types of OVAW and exploring variation across these contextual variables.\u003c/p\u003e\u003cp\u003eDespite global attention to OVAW, important gaps remain in Bangladesh. First, studies often treat OVAW generically, without distinguishing among offense types, though impersonation, hate speech, and doxing differ in prevalence and impact (Ahmed et al., 2017; Monni \u0026amp; Sultana, 2016). Second, while research documents depression, anxiety, or social withdrawal, these effects are rarely studied together, leaving the interplay between mental, behavioral, and physical outcomes underexplored (Vandenbosch \u0026amp; van Oosten, 2017; Mridha et al., 2024). Third, contextual variation—urban vs. rural residence, internet use intensity, and occupational status—remains largely absent from scholarship (Sheikh et al., 2023). Finally, most studies use either small-scale qualitative data or broad cross-sectional surveys, limiting both generalizability and depth. Few have applied mixed-methods designs to bridge these gaps. This study addresses these shortcomings by examining disaggregated forms of OVAW, linking them with varied outcomes, and situating them within Bangladesh’s distinct sociocultural landscape.\u003c/p\u003e\u003cp\u003eWhile there have been previous global and Bangladeshi studies reporting the prevalence and harms of OVAW, most have treated these as extensions of existing gender-based violence frameworks without explaining how local patriarchal systems shape their digital expressions. Very few existing models account for how norms around \"honor\" transform digital harassment into reputational or relational strain or how such effects vary across rural and occupational contexts. In an effort to fill these theoretical and contextual gaps, this study combines General Strain Theory with Intersectional Feminism in conceptualizing OVAW as an honor-mediated form of digital strain. This approach not only extends GST to non-Western patriarchal settings but also presents a culturally located explanation of how online abuse reproduces structural inequalities within the context of low- and middle-income countries like Bangladesh.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTheoretical Frameworks\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eGeneral Strain Theory (GST) and Intersectional Feminism are used as complementary theoretical frameworks in this study to account for OVAW in Bangladesh. The theories are chosen for their promise in explaining the psychological, behavioral, and structural aspects of OVAW, consistent with the aims of the research to determine common forms of OVAW, measure their effects, and determine social and demographic differences. GST accounts for how OVAW produces individual-level strains that result in psychological and behavioral outcomes, and Intersectional Feminism positions these outcomes in the patriarchal and intersectional sociocultural setting of Bangladesh. Combined, they provide a strong basis for informing the methodology, explaining expected findings, and guiding policy interventions.\u003c/p\u003e\u003cp\u003eGeneral Strain Theory, formulated by Agnew (1992, 2006), argues that strains—events or conditions that are viewed as negative, like the inability to attain desired goals, losing positive stimuli, or experiencing negative stimuli—lead to negative emotions like depression, anxiety, or fear. Such emotions lead to coping mechanisms, like withdrawal or avoidance, as the individual tries to deal with or escape the strain. Strains are of three types: (a) failure to attain goals (e.g., loss of social standing), (b) blockage of opportunity to gain positive stimuli (e.g., loss of credibility), and (c) presentation of aversive stimuli (e.g., harassment). In OVAW, strains are caused by activities such as impersonation, which threatens personal reputation, or hate speech, which generates fear of social disapproval. In Bangladesh, sociocultural norms of female \"honor\" and victim-blaming compound these pressures, amplifying psychological distress and social retreat (Islam \u0026amp; Rahman, 2023). For instance, a young female victim of online harassment can become fearful or restrict her online activities to prevent being targeted again, illustrating GST's strain-coping pathway. GST is core to this research inasmuch as it underpins the aim to explore OVAW's behavioral and psychological effects, informing survey and interview questions on mental health (e.g., depression) and behavioral reactions (e.g., avoidance), and offering a framework for explaining how various forms of OVAW have disparate effects.\u003c/p\u003e\u003cp\u003eIntersectional Feminism, building on Crenshaw's (1989) theorization and developed further by researchers of technology-facilitated violence (Henry \u0026amp; Powell, 2018), theorizes OVAW as an expression of patriarchal power relations, informed by intersecting systems of oppression, such as gender, socioeconomic status, and geographical location. It contends that online spaces mirror offline gender inequalities, compounded by anonymity and access, which facilitate gendered abuse. In Bangladesh, patriarchal values upheld by religious conservatism and honor culture compound the effect of OVAW through exacerbating victim-blaming and deterring reporting (Islam \u0026amp; Rahman, 2023). Intersectionality also highlights the ways in which dimensions such as rural/urban locality or working status condition vulnerability. For example, rural women are likely to be more vulnerable on account of lower digital literacy, whereas working women might be targeted for defying traditional gender expectations. This viewpoint legitimates the study's aim to investigate demographic differences in OVAW experiences, guiding the incorporation of variables such as occupation and residence in data gathering and highlighting structural obstructions, such as the lack of cybercrime legislation, that facilitate abuse.\u003c/p\u003e\u003cp\u003eIntegrated Framework and Applicability to the Study\u003c/p\u003e\u003cp\u003eGST and Intersectional Feminism constitute a unified framework on individual and systemic levels of OVAW. GST specifies how OVAW strains, e.g., reputational damage or fear of stigma, are transduced to psychological distress and behavioral reactions, and Intersectional Feminism specifies how patriarchal norms and intersectional conditions like rural dwelling or work status shape these effects. The model is consistent with this study's aims: operationalizing OVAW types (Intersectional Feminism), measuring their effects (GST), and examining demographic variation (Intersectional Feminism). It directs the mixed-methods design to inform qualitative interviews for collecting strain-related experiences (GST) and quantitative surveys for investigating demographic patterns (Intersectional Feminism) (Creswell \u0026amp; Plano Clark, 2018). Thematic analysis of interviews will determine strain and patriarchal themes (Braun \u0026amp; Clarke, 2019), whereas quantitative analyses will examine contextual influences. The model guides policy suggestions, for instance, mental health care for strains (GST) and setting-based interventions for marginalized groups (Intersectional Feminism), to a sophisticated understanding of OVAW as well as interventions for gender-equitable digital citizenship in Bangladesh.\u003c/p\u003e"},{"header":"Research Methodology","content":"\u003cp\u003eThis study is thus designed in an exploratory sequential mixed-methods manner to capture both the depth of the lived experiences and the breadth of the emerging patterns of OVAW in Bangladesh, as provided for in Creswell and Plano Clark (2018). In this design, the qualitative phase precedes and informs the quantitative phase in the process of concept generation and instrument development that is clearly grounded in context-specific realities.\u003c/p\u003e\u003cp\u003ePhase 1 involved semi-structured interviews with young women who had personally experienced online violence, in order to probe into forms, language, and perceived consequences of OVAW, and to identify salient cultural mechanisms, such as honor-based stigma and reputational harm, that shape these experiences. Themes extracted in this phase directly informed the structure, wording, and item categories of the subsequent survey questionnaire, ensuring cultural and linguistic relevance. In Phase 2, a larger population of women took part in a cross-sectional online survey assessing the frequency of the identified patterns, and variation by selected social and demographic factors. Integration across the two phases occurred at two levels:\u003c/p\u003e\u003cp\u003eDuring the instrument development process, through which qualitative codes and illustrative narratives were transformed into measurable variables such as impersonation, hate speech, and withdrawal from public engagement.\u003c/p\u003e\u003cp\u003eDuring interpretation, where qualitative insights helped explain and contextualize important statistical associations, for example when interviews revealed that it was community gossip and family honor which drove the impersonation experiences of rural women, thus explaining quantitative disparities across rural-urban respondents.\u003c/p\u003e\u003cp\u003eThis sequential linking ensured that the qualitative exploration was grounded in quantitative measurement, while statistical analyses served to widen the frames within which qualitative narratives could be interpreted.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSampling and Participants\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePurposive sampling was used to find people who had personally experienced online harassment or abuse. Since women who experience digital abuse are a hidden and stigmatized population that is challenging to reach through random or chance techniques, the purposeful recruitment was acceptable (Etikan et al., 2016; Palinkas et al., 2015). Social media groups, professional associations, and academic networks were used to recruit participants, and invitations to participate in the study were extensively disseminated with guarantees of secrecy. \u003c/p\u003e\u003cp\u003eBetween July and August of 2024, a total of 204 replies were gathered via Google Forms; after cleaning and quality check, 202 responses remained. Students and working women from both rural and urban locations were included in the sample. Online purposive sampling naturally restricts external validity, even as it made it possible to reach scattered respondents at a lower cost and with more anonymity than would be achievable in person for delicate subjects. Therefore, rather than being statistically representative of all Bangladeshi women, these results should be seen as reflecting young, digitally active women who willingly volunteered their experiences.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Collection Procedures\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePhase 1: Qualitative Interviews.\u003c/p\u003e\u003cp\u003eSemi-structured interviews were carried out in confidential online sessions. Participants were asked about the types of online abuse they experienced, perceived motivations of perpetrators, coping strategies, and emotional or social consequences. The interviews were recorded with consent and then transcribed verbatim for thematic analysis.\u003c/p\u003e\u003cp\u003ePhase 2: Quantitative Survey.\u003c/p\u003e\u003cp\u003eThe survey instrument was developed from Phase 1 codes, structured into four sections: a) demographic characteristics; b) experiences of specific OVAW types, including impersonation, hate speech, and sextortion; c) psychological, behavioral, and physical impacts; d) and contextual factors such as intensity of internet use, occupation, and residence. Both nominal and ordinal scales were used to measure the items. The analysis made use of both descriptive and inferential statistics. \u003cbr\u003e \u003cbr\u003e SPSS v29 was used to examine quantitative data. Chi-square tests evaluated correlations between demographic data, while descriptive statistics detailed prevalence and consequence patterns. Standardized residuals were used to identify the categories that drove significant relationships (Sharpe, 2015). In order to transition from the original open coding to axial themes related to strain, stigma, and intersectional inequality, thematic analysis was carried out in accordance with Braun and Clarke (2019). To develop a comprehensive knowledge of the mechanisms of OVAW in the Bangladeshi sociocultural context, the results from both strands were interpreted together.\u003c/p\u003e\u003cp\u003eEthical Considerations: Consenting was secured electronically from all participants after providing information on research objectives, voluntary participation, and the right to withdraw at any stage. Identities and responses were anonymised, and all questions relating to personal experiences were optional to minimize distress. Contact information for hotlines for mental health support was also provided to participants at the end of every session. Limitations The results, therefore, are indicative rather than generalizable to the whole population of Bangladeshi women, given the purposive and online self-selection approach. Self-reporting and cross-sectional design further restrict causal inference. Future studies should consider stratified or probability sampling, offline inclusion strategies, and longitudinal designs to enhance representativeness and temporal validity.\u003c/p\u003e"},{"header":"Findings","content":"\u003cp\u003e\u003cstrong\u003eMost prevalence of Online Violence \u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe study used descriptive statistics to identify the prevalence of types of violence that have predominantly occurred online. Table 1 shows that descriptive statistics on the prevalence of experiences of online violence among young women and girls (N=202). The most common type of online violence found was impersonation (n = 78), with a mean score of .39 (38.6%) and a standard deviation of .488. The second and third most common online violence were hate speech (n=56), with a mean of. 28 and a standard deviation of .449, and offensive comments (n=34) with a standard deviation of .375. There were fewer reports of stalking (n=14), indicating 6.9% with a standard deviation of. 255, doxing and sextortion were 4% for each type, which indicates that these forms are relatively uncommon. Moreover, impersonation and hate speech are significantly more common than doxing and sextortion among young women and girls who have experienced some form of online violence.\u003c/p\u003e\u003cp\u003eTo test the significant differences in experiencing online VAW based on internet use, urbanity/rurality and occupations, this study used the Chi-square test of association. The chi-square tests in table 2 showed how the experience and incidence of online violence types were significantly different based on internet usage, location (urban vs rural), and occupation (students vs employed). Internet usage had shown significant associations with frequencies of experiencing violence (X²= 49.439, p =\u0026lt;.001), offensive comments (X²= 10.023, p =. 007), impersonation (X²= 24.247, p \u0026lt;.001), hate speech (X² = 27.935, p \u0026lt;. 001), and stalking (X²= 10.654, p =. 005). However, the relationships are significant in doxing(X²= 3.014, p = .222). This result indicates that there were significant differences in the experience of violence based on internet usage.\u003c/p\u003e\u003cp\u003eThe post hoc tests were performed to determine how frequencies of internet usage were related to violence types employing Standardized Residual greater than 1.96. The results show low and high internet users (standardized residual = 2.3 and 3.9) were associated with low and high frequencies of violence incidence. Moderate internet usage was related to moderate frequencies of violence incidence (standardized residual = 2.6). Moreover, low internet usage was also associated with offensive comment (standardized residual = 2.4) and stalking (standardized residual = 2.9). While moderate internet usage was associated with hate speech (standardized residual = 3.00), higher internet usage was associated with impersonation(standardized residual = 2.9\u003cstrong\u003e). \u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThis study also tested how online violence was related to locations, whether young women live in rural and urban areas. The result found that impersonation (X²= 25.321, p \u0026lt;.001), doxing (X²= 7.695, p =. 006), and hate speech (X²=9.805, p \u0026lt;.002) were found significant. However, no significant relationships were found for frequencies of experiencing violence, (X²=3.506, p =.173), offensive comments (X² = 0.986, p =. 321), sextortion (X²= 3.689, p =. 055), and stalking (X²= 0.032, p =. 858). Post hoc tests showed impersonation (standardized residual = 3.3) and hate speech (standardized residual = 2.2) were higher in rural areas than urban areas.\u003c/p\u003e\u003cp\u003eFor occupations whether young women are currently student or employed, the result showed that relationship of frequencies of violence (X²= 65.197, p \u0026lt;. 001), offensive comment (violence (X²= 12.633, p \u0026lt;. 002), impersonation (X²= 33.601, p \u0026lt;. 001), and hate speech (X²= 83.296, p \u0026lt;. 001) were significant. Doxing, sextortion and stalking were not significant relationships with occupations. Post hoc test showed while the incidence of violence, offensive comment and impersonation among student and young employed women were the same, employed women have moderate frequencies of violence than student (standardized residual =3.3). In addition, hate speech is higher in employed women than students (standardized residual=6.8).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsequences of online violence\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe descriptive statistics also show the most prevalent consequences of online violence against young women.\u003c/p\u003e\u003cp\u003eTable 3 provides descriptive statistics about the most common forms of online violence perpetrated against young women. The most common consequence of online violence was avoidance of participating in public spaces (n=126) with a mean score of 62 and a standard deviation of .486. Mental depression was reported 61.4% of young women with a standard deviation of .48807, which was followed by behavioral issues (31.7%) with them with a standard deviation of 1.075 and declining trust on others (20.8%) with a standard deviation of .40683. Finally, 11.9% of young women also mentioned about withdrew from online activities with a standard deviation of. 32437 and 5.9% indicated physical-related stress with a standard deviation of .23697.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsequences of online violence internet use, urbanity/rurality and occupations\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe researcher also tested if there were any significant differences in effects on health based on internet use, urbanity/rurality and occupations (Chi-square test of association).\u003c/p\u003e\u003cp\u003eThe test results also showed that the degree of health effects from online violence varies significantly depending on internet usage, rural or urban, and student or employed (table 4). Trust decline on others had strong associations with internet usage, X²= 31.839, p \u0026lt;.002; physical related stress, X²= 10.932, p =. 004; mental depression, X² = 58.678, p \u0026lt;. 001; withdrawal from online, X² = 16.021, p \u0026lt;.001; and behavior, X² = 26.422, p \u0026lt;.001. No significant association was found for avoidance of public space participation, X²= 1.158, p = .560 suggesting this consequence was less correlated with internet usage.\u003c/p\u003e\u003cp\u003eThe results of post hoc tests showed that trust erosion in other people was associated with low internet usage (standardized residual=2.6) and higher internet usage (standardized residual=2.6). While physical related stress (standardized residual=2.5), and withdrawal from online (standardized residuals=2.9) were associated with high internet usage, the mental depression was associated with moderate and high internet usages (standardized residuals=3.3 and 3.5).\u003c/p\u003e\u003cp\u003eIn terms of locations, this study found significant associations for trust decreased, X²=7.141, p \u0026lt;.008; physical related stress, X²= 16.619, p \u0026lt; .001; mental depression, X²= 47.776, p \u0026lt; .001; withdrawal from the internet, X²= 16.569, p \u0026lt;.001; and behavioral problems, X² = 15.698, p =.001. 0.001, indicating that urban residents are more likely to face these health-related consequences than rural residents. However, there was no significant association for avoidance of participation in public space, X²= 2.166, p =. 141.\u003c/p\u003e\u003cp\u003eThe post hoc tests also showed that decreased trust on others (standardized residual=2.00), physical related stress (standardized residual=3.30), withdrawal from online (standardized residual=3.20), and moderate behavioral problems (standardized residual=2.30) were higher in rural areas than urban areas. However, mental depression (standardized residual=2.40) was found higher in urban areas than rural areas.\u003c/p\u003e\u003cp\u003eBased on occupational status, there were statistically significant differences in trust decreased significantly, X² = 17.442, p \u0026lt;. 001; mental depression, X²= 26.329, p \u0026lt;. 001; and behavioral, X²= 24.424, p \u0026lt;. 001. However, there were no significant associations for physical-related stress, X²= 0.507, p =.776; withdrawal from online, X²= 3.974, p =. 137, or avoidance of accessing public space, X² = 5.767, p =. 056. The post hoc tests showed mental depression (standardized residual=2.70) was higher in employed women than students.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsequences of violence across various types of online violence\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThis study also performed a Chi-square test of association to demonstrate how the consequences of violence differ across various types of online violence. Table 5 showed the association between different forms of online violence against young women and mental, behavioral and physical consequences. Offensive comments were significant relationships with behavioral issues (X² =13.048, p =. 005) and avoidance of participating in public places (X² =6.952, p =. 008). In addition, impersonation was associated with a decline in trust in others (X² = 4.240, p =. 039), physical-related stress (X² = 4.236, p =. 040), mental depression (X² = 22.222, p =\u0026lt;.001), withdrawal from online (X² = 9.042, p =003), behavioral issues (X² = 26.485, p =\u0026lt;.001) and avoidance of public involvement (X² = 6.664, p = .010). Furthermore, hate speech decreased in trust (X² =4.77, p =.029), mental depression (X² =9.65, p =.002), and behavioral issues (X² =13.368, p =.004). However, doxing and stalking were not found to have a statistically significant association with any kind of online offense.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOnline Violence Against Women (OVAW) in Bangladesh is not simply a product of being exposed to the Internet. While quantitative data indicates that rural and lower frequency users experience more impersonation and hate speech than urban and/or higher frequency users, qualitative data reveals that the honor-based stigma which exists in Bangladesh, transforms any false information, regardless of whether it is an impersonation or a fabrication of any kind, into permanent damage to one's reputation and/or credibility. In addition, the limited digital literacy skills that exist for women in Bangladesh, make it difficult for women to detect, let alone respond to, fake profiles. Using the General Strain Theory (GST)\u0026ndash;Intersectional Feminist (IF) lens as a framework, impersonation can be seen as a culturally specific form of strain that directly jeopardizes a woman's \"honor,\" which is one of the most important social currencies for women. However, when considering intersectional disadvantages, such as living in rural areas, having a lower level of education, working outside the home, etc., these same disadvantages also limit access to coping resources, thereby increasing both the prevalence and harm of OVAW (Agnew, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Crenshaw, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Henry \u0026amp; Powell, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the dominant forms of OVAW reported globally are impersonations and hate speech (Citron, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dragiewicz et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ging \u0026amp; Siapera, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); these types of harassment have unique meanings in Bangladesh. For example, the ease with which anonymous accounts can be created on-line and the lack of consistent enforcement of the Digital Security Act (DSA), makes impersonation easier to occur (Amnesty International, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rahman, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hate speech in Bangladesh, is similar to other forms of hate speech globally; however, hate speech in Bangladesh, often reflects moral policing and existing gendered hierarchies found in Bangladesh (Sultana \u0026amp; Islam, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As expected, working women and rural women were significantly more likely to report experiencing higher levels of OVAW, as they have been identified as experiencing the greatest backlash from feminists when women deviate from their assigned traditional roles (Jane, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lewis et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, the unanticipated vulnerability of low frequency users, further emphasizes how the gender digital divide operates as a structural risk factor for OVAW (Antonio \u0026amp; Tuffley, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fatehkia et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe psychological and social impacts of OVAW in Bangladesh, align with international findings; however, they indicate sharp silencing effects: High levels of depression, anxiety, and distrust, caused approximately 62% of OVAW victims to avoid using public space (Reed et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Suzor et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vickery \u0026amp; Everbach, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although the avoidance of public spaces due to fear of encountering someone who will harass them on line has been documented internationally; the extent to which this occurs in Bangladesh, is far greater than has been documented in Western countries, demonstrating how the combination of digital victimization and patriarchal expectations to stay out of the public eye, results in the erosion of women's civic participation and digital citizenship (Baer, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mendes et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the many contributions made by this study, there are several limitations. First, the cross-sectional design of the study, limits the ability to determine causality between the variables studied. Second, the sample of the study was primarily composed of students from universities in Bangladesh and snowballing methods used to recruit participants, may have resulted in an underrepresentation of older women, women who are less educated, and women who do not use the internet. Third, the self-report measures of OVAW, may have been subject to recall bias and/or social-desirability bias, especially regarding sensitive topics related to honor. Fourth, the relatively small size of the qualitative sample (n\u0026thinsp;=\u0026thinsp;28) limits the degree to which findings can be generalized across all regions of Bangladesh. Therefore, future research should use longitudinal study designs, draw samples from larger populations, and include non-internet using women to fully examine the spectrum of risk for OVAW in Bangladesh.\u003c/p\u003e \u003cp\u003eIn conclusion, OVAW in Bangladesh represents a culturally embedded form of patriarchal control over women, based upon honor-based social norms, digital inequality, and intersectional disadvantage. The findings of this study contribute to our understanding of the global phenomenon of technology facilitated gender-based violence in Low- and Middle-Income Countries (LMICs), and emphasize the necessity for developing contextually relevant and systemic interventions that address both the digital harms that result from OVAW and the offline systems that enable these harms to continue.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePolicy Recommendation\u003c/h2\u003e \u003cp\u003eYoung women in Bangladesh experiencing online violence deserve immediate attention and coordination. Laws regarding cybercrime exist however there is no strong enforcement mechanism; therefore police units with digital forensic expertise must be created along with anonymous, confidential reporting mechanisms for victims to prevent stigma. The same applies to incorporating digital safety into school curriculum as well as using basic community training in remote areas where less frequent Internet users have a greater risk of being targeted. All educational institutions and workplaces must create and enforce clearly defined anti-harassment policy that provide support (grievance procedures, peer support groups, etc.) and that include local leaders who speak out. Accessible mental health services (hotlines, NGO counseling, etc.) must also be provided to assist survivors to both cope with their experience and continue participating in online activities. Overall, all sectors (government, technology companies, educational institutions, civil society) must work together to develop a national database of all reported incidents and address the underlying societal norms of patriarchy and \"honor\" based social structures that silence women. Only when all parties involved (enforcement agencies, educators, technology companies, civil society, etc.) make an effort to promote awareness (education), support (access to resources), and cultural change will young Bangladeshi women be able to fully engage and contribute equally to digital activities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAs an increasing number of people rely on digital technology to meet their most fundamental requirements, get information, and build social connections, it is also important to note that the digital environment represents new threats to both the safety of individuals, and their human rights. In Bangladesh, for example, research has shown that the technology-enabled Gender-Based Violence (GBV) is a widespread, gendered phenomenon which includes various forms of assault, abuse and harassment. Online Violence Against Women (OVAW), which encompasses cyber-bullying, sexual harassment, image-based abuse, threat messaging, and gendertrolling, predominantly impacts women who are visible in the public sphere. Further research is needed to fully understand the prevalence, manifestation, and impact of OVAW in Bangladesh due to the significant increase in prevalence over the last few years. Collaborative efforts are necessary to address the OVAW issue among all stakeholders, such as governments, law enforcement officials, NGOs/Civil Society Organizations, ICT companies, and researchers. If these actors share a common, yet context-specifically nuanced, understanding of this complex problem; then they may collaborate effectively to prevent, reduce, and respond to the Technology Facilitated GBV in a timely and trauma-informed manner.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003eFunding statement: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/li\u003e\n \u003cli\u003eDeclaration of conflicting interests: The Author declares that there is no financial and/or non-financial conflict of interest.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAI Statement: The author did not use AI tools during preparation of the manuscript.\u003c/li\u003e\n \u003cli\u003eAs the data was collected anonymously, so we didn\u0026rsquo;t take any ethical approval from any institution.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eConsenting was secured electronically from all participants after providing information on research objectives and all the respondents are taken anonymously.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe article is not under consideration for publication elsewhere.\u003c/li\u003e\n \u003cli\u003eThis article received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/li\u003e\n \u003cli\u003eThe Authors declare that there is no conflict of interest.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe author does not use generative AI in preparing this manuscript.\u003c/li\u003e\n \u003cli\u003eThe author declares that there are no hazards and human or animal subjects involved.\u003c/li\u003e\n \u003cli\u003eThe authors have written entirely original works and cited other works appropriately.\u003c/li\u003e\n \u003cli\u003eAll participants provided informed consent prior to participation.\u003c/li\u003e\n \u003cli\u003eClinical trial number: not applicable.\u003c/li\u003e\n \u003cli\u003eThere\u0026rsquo;s no competing interest.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Freely given, informed consent to participate in the study was obtained from all participants prior to data collection. Participants were informed about the purpose of the study, the voluntary nature of participation, potential risks and benefits, and their right to withdraw at any time without consequence.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cu\u003eEthics Statement\u003c/u\u003e\u003c/strong\u003e\u003cu\u003e\u003cbr\u003e\u0026nbsp;This study involved the collection of anonymous survey data from human participants and was conducted in accordance with the ethical principles of the Declaration of Helsinki. Participation was voluntary, and no personally identifiable information was collected. The requirement of formal ethical approval for this study was waived by the Departmental Research Ethics Committee, Department of Public Administration, University of Chittagong, in accordance with national guidelines and the Declaration of Helsinki, due to the minimal-risk nature of the study and the use of fully anonymized data.\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cu\u003eConsent to publish\u003c/u\u003e\u003c/strong\u003e\u003cu\u003e\u003cbr\u003e\u0026nbsp;Informed consent for publication of anonymized data was obtained from all participants as part of the informed consent process before participation.\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cu\u003eData Availability\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cu\u003eThe datasets generated during and/or analyzed during the current study are not publicly available due to the sensitive nature of the data involving experiences of online violence and mental health, which could potentially compromise participant anonymity and privacy, but are available from the corresponding author on reasonable request.\u003c/u\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlotaibi, N. B. (2019). Cyber bullying and the expected consequences on the students\u0026rsquo; academic achievement. IEEE access, 7, 153417-153431.\u003c/li\u003e\n\u003cli\u003eAgnew, R. (1992). Foundation for a general strain theory of crime and delinquency. Criminology, 30(1), 47\u0026ndash;87. https://doi.org/10.1111/j.1745-9125.1992.tb01093.x\u003c/li\u003e\n\u003cli\u003eAgnew, R. (2006). Pressured into crime: An overview of general strain theory. Oxford University Press.\u003c/li\u003e\n\u003cli\u003eAhmed, S., Kabir, A., Sharmin, S., \u0026amp; Jafrin, S. (2017). Cyber-crimes against womenfolk on social networks: Bangladesh context. International Journal of Computer Applications, 174(4), 9\u0026ndash;15. https://doi.org/10.5120/ijca2017915407\u003c/li\u003e\n\u003cli\u003eAmin, R. (2024). Causes and consequences of cyberbullying against women in Bangladesh: A comprehensive study. SALASIKA: Indonesian Journal of Gender, Women, Child, and Social Inclusion Studies, 6(2), 127\u0026ndash;145. https://doi.org/10.36625/sj.v6i2.123\u003c/li\u003e\n\u003cli\u003eAmnesty International. (2019). \u003cem\u003eBangladesh: Surveillance and repression under the Digital Security Act\u003c/em\u003e. https://www.amnesty.org/en/documents/asa13/1422/2019/en/\u003c/li\u003e\n\u003cli\u003eAntonio, A., \u0026amp; Tuffley, D. (2014). The gender digital divide in developing countries. \u003cem\u003eFuture Internet\u003c/em\u003e, 6(4), 673\u0026ndash;687. https://doi.org/10.3390/fi6040673\u003c/li\u003e\n\u003cli\u003eBaer, H. (2016). Redefining rape culture: Online feminist activism against sexual violence. \u003cem\u003eFeminist Media Studies\u003c/em\u003e, 16(4), 634\u0026ndash;650. https://doi.org/10.1080/14680777.2016.1138234\u003c/li\u003e\n\u003cli\u003eBangladesh Telecommunication Regulatory Commission. (2023). Internet subscriber statistics in Bangladesh. https://www.btrc.gov.bd/site/page/0ae3ff81-68d7-4dd6-8b52-9b1c26102480/-\u003c/li\u003e\n\u003cli\u003eBansal, V., Rezwan, M., Iyer, M., Leasure, E., Roth, C., Pal, P., \u0026amp; Hinson, L. (2024). A scoping review of technology-facilitated gender-based violence in low- and middle-income countries across Asia. \u003cem\u003eTrauma, Violence, \u0026amp; Abuse\u003c/em\u003e, 25(1), 463\u0026ndash;475. https://doi.org/10.1177/15248380231154614\u003c/li\u003e\n\u003cli\u003eBansal, N. (2024). Online harassment and violence against women in Asia-Pacific: A scoping review. Trauma, Violence, \u0026amp; Abuse, 25(3), 678\u0026ndash;695. https://doi.org/10.1177/15248380231154614\u003c/li\u003e\n\u003cli\u003eBarker, k., \u0026amp; Jurasz,O. (2020). Online violence against women as an obstacle to gender equality: a critical view from Europe. European Equality Law Review, 2020 (1), 47-60.\u003c/li\u003e\n\u003cli\u003eBlackwell, L., Lo, K., \u0026amp; Marwick, A. E. (2023). How platforms moderate online abuse: Research perspectives and policy implications. Social Media + Society, 9(1). https://doi.org/10.1177/20563051231157297\u003c/li\u003e\n\u003cli\u003eBraun, V., \u0026amp; Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589\u0026ndash;597. https://doi.org/10.1080/2159676X.2019.1628806\u003c/li\u003e\n\u003cli\u003eBraekman, E., Vandenheede, H., Charafeddine, R., \u0026amp; Demarest, S. (2022). Comparing web-based and face-to-face survey modes in health research: Cost, ease, and representativeness. Journal of Medical Internet Research, 24(1), e30366. https://doi.org/10.2196/30366\u003c/li\u003e\n\u003cli\u003eCaridade, S. M. M., Dinis, M. A., \u0026amp; Pereira, V. E. (2022). The predictive role of psychological vulnerability and sense of security in cybervictimization in adolescents. \u003cem\u003eJournal of Interpersonal Violence\u003c/em\u003e, 37(23\u0026ndash;24), NP22731\u0026ndash;NP22756. https://doi.org/10.1177/08862605211063011\u003c/li\u003e\n\u003cli\u003eCeluch, M., Latikka, R., Oksa, R., Savela, N., \u0026amp; Oksanen, A. (2023). Self-censorship among online harassment targets: The role of support at work, harassment characteristics, and the target\u0026rsquo;s public visibility. \u003cem\u003eInformation, Communication \u0026amp; Society\u003c/em\u003e. Advance online publication. https://doi.org/10.1080/1369118X.2023.2289978\u003c/li\u003e\n\u003cli\u003eCitron, D. K. (2014). \u003cem\u003eHate crimes in cyberspace\u003c/em\u003e. Harvard University Press. https://doi.org/10.4159/harvard.9780674735616\u003c/li\u003e\n\u003cli\u003eCrenshaw, K. (1989). Demarginalizing the intersection of race and sex: A Black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum, 1989(1), 139\u0026ndash;167.\u003c/li\u003e\n\u003cli\u003eCrenshaw, K. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. \u003cem\u003eStanford Law Review\u003c/em\u003e, 43(6), 1241\u0026ndash;1299. https://doi.org/10.2307/1229039\u003c/li\u003e\n\u003cli\u003eCreswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage Publications.\u003c/li\u003e\n\u003cli\u003eDavidson, J., Suzor, N., Dragiewicz, M., Harris, B., Gillett, R., Burgess, J., \u0026amp; Van Geelen, T. (2019). Human rights by design: The responsibilities of social media platforms to address gender-based violence online. Policy \u0026amp; Internet, 11(1), 84\u0026ndash;103. https://doi.org/10.1002/poi3.185 Academia\u003c/li\u003e\n\u003cli\u003eDouglas, D. M., McGarty, C., \u0026amp; Lyddy, F. (2019). The psychology of online harassment. Journal of Social Issues, 75(1), 1\u0026ndash;16. https://doi.org/10.1111/josi.12303\u003c/li\u003e\n\u003cli\u003eDragiewicz, M., Burgess, J., Matamoros-Fern\u0026aacute;ndez, A., Salter, M., Suzor, N. P., Woodlock, D., \u0026amp; Harris, B. (2018). Technology facilitated coercive control: Domestic violence and the competing roles of digital media platforms. \u003cem\u003eFeminist Media Studies\u003c/em\u003e, 18(4), 609\u0026ndash;625. https://doi.org/10.1080/14680777.2018.1447346\u003c/li\u003e\n\u003cli\u003eEtikan, I., Musa, S. A., \u0026amp; Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1\u0026ndash;4. https://doi.org/10.11648/j.ajtas.20160501.11\u003c/li\u003e\n\u003cli\u003eEuropean Institute for Gender Equality. (2017). Cyber violence against women and girls \u0026ndash; Key terms and concepts. https://eige.europa.eu/publications/cyber-violence-against-women-and-girls-key-terms-and-concepts\u003c/li\u003e\n\u003cli\u003eFardouly, J., Magson, N. R., Rapee, R. M., Johnco, C. J., \u0026amp; Oar, E. L. (2023). The impact of social media use on appearance self-esteem from early to late adolescence: An eye-tracking study. \u003cem\u003eBody Image\u003c/em\u003e, 44, 161\u0026ndash;170. https://doi.org/10.1016/j.bodyim.2022.12.007\u003c/li\u003e\n\u003cli\u003eFatehkia, M., Kashyap, R., \u0026amp; Weber, I. (2018). Using Facebook ad data to track the global digital gender gap. \u003cem\u003eWorld Development\u003c/em\u003e, 107, 189\u0026ndash;200. https://doi.org/10.1016/j.worlddev.2018.03.007\u003c/li\u003e\n\u003cli\u003eFelten, J. (2023). Violence against women and girls as a public health crisis during humanitarian emergencies. Journal of International Humanitarian Action, 8(1), 1\u0026ndash;10. https://doi.org/10.1186/s41018-023-00142-5\u003c/li\u003e\n\u003cli\u003eG\u0026aacute;mez-Guadix, M., Mateos-P\u0026eacute;rez, E., \u0026amp; Cuervo-Ortega, S. (2023). Technology-facilitated sexual violence victimization: Prevalence, risk factors, and relationship with offline sexual violence among adolescents. Computers in Human Behavior, 141, Article 107610. https://doi.org/10.1016/j.chb.2022.107610 \u003c/li\u003e\n\u003cli\u003eG\u0026aacute;mez-Guadix, M., Borrajo, E., \u0026amp; Calvete, E. (2019). Cybervictimization and mental health in adolescents: A systematic review. \u003cem\u003eAggression and Violent Behavior\u003c/em\u003e, 49, 101326. https://doi.org/10.1016/j.avb.2019.101326\u003c/li\u003e\n\u003cli\u003eGing, D., \u0026amp; Siapera, E. (2018). Special issue on online misogyny. \u003cem\u003eFeminist Media Studies\u003c/em\u003e, 18(4), 515\u0026ndash;524. https://doi.org/10.1080/14680777.2018.1483379\u003c/li\u003e\n\u003cli\u003eGlobal Fund for Women. (2015). Report on cross-cultural program and policy design to address online violence against women. Global Fund for Women. Retrieved from https://www.globalfundforwomen.org\u003c/li\u003e\n\u003cli\u003eHegarty, K., Tarzia, L., \u0026amp; Valpied, J. (2021). Depression and post-traumatic stress disorder in women exposed to intimate partner violence: A longitudinal study. \u003cem\u003eAustralian \u0026amp; New Zealand Journal of Psychiatry\u003c/em\u003e, 55(5), 497\u0026ndash;507. https://doi.org/10.1177/0004867420963745\u003c/li\u003e\n\u003cli\u003eHenry, N., Flynn, A., \u0026amp; Powell, A. (2024). Technology-facilitated sexual violence: A review of the evidence and policy responses. Trends in Organized Crime, 27(1), 1\u0026ndash;22. https://doi.org/10.1007/s12117-023-09512-5 \u003c/li\u003e\n\u003cli\u003eHenry, N., \u0026amp; Powell, A. (2015). Embodied harms: Gender, shame, and technology-facilitated sexual violence. Violence Against Women, 21(6), 758\u0026ndash;779. https://doi.org/10.1177/1077801215576581\u003c/li\u003e\n\u003cli\u003eHenry, N., \u0026amp; Powell, A. (2018). Technology-facilitated sexual violence: A literature review of empirical research. Trauma, Violence, \u0026amp; Abuse, 19(2), 195\u0026ndash;208. https://doi.org/10.1177/1524838016650189\u003c/li\u003e\n\u003cli\u003eHenry, N., Flynn, A., \u0026amp; Powell, A. (2023). Technology-facilitated sexual violence: A multidisciplinary approach to developing literature and informing practice. \u003cem\u003eTrauma, Violence, \u0026amp; Abuse\u003c/em\u003e, 24(2), 313\u0026ndash;323. https://doi.org/10.1177/15248380211043296\u003c/li\u003e\n\u003cli\u003eHellevik, S. (2025). Gendered online harassment of politicians and journalists: A global systematic review. Journal of Communication, 75(1), 34\u0026ndash;56. https://doi.org/10.1093/joc/jqab021\u003c/li\u003e\n\u003cli\u003eHinduja, S. (2025). Cyberbullying through the lens of trauma: Exploring the role of exclusion, rejection, and PTSD symptoms. BMC Public Health, 25(1), Article 22692. https://doi.org/10.1186/s12889-025-22692-6\u003c/li\u003e\n\u003cli\u003eIslam, R., \u0026amp; Rahman, M. (2023). Cultural norms and the silencing of violence against women in Bangladesh. Asian Journal of Women\u0026rsquo;s Studies, 29(1), 77\u0026ndash;95. https://doi.org/10.1080/12259276.2023.2173681\u003c/li\u003e\n\u003cli\u003eIroegbu, M. (2024). Investigating the psychological impact of cyber-sexual harassment (CSH) in adult women. International Journal of Environmental Research and Public Health, 21(4), 112837. https://doi.org/10.3390/ijerph21040123\u003c/li\u003e\n\u003cli\u003eJane, E. A. (2017). \u003cem\u003eMisogyny online: A short (and brutish) history\u003c/em\u003e. SAGE Publications. https://doi.org/10.4135/9781473916029\u003c/li\u003e\n\u003cli\u003eKamal, S. B. (2024). Women victimization in social networking sites in Bangladesh. International Journal of Innovative Science and Research Technology, 9(11), 1047\u0026ndash;1052. https://doi.org/10.38124/ijisrt/ijisrt24nov447\u003c/li\u003e\n\u003cli\u003eKowalski, R. M., Giumetti, G. W., Schroeder, A. N., \u0026amp; Lattanner, M. R. (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140(4), 1073\u0026ndash;1137. https://doi.org/10.1037/a0035618\u003c/li\u003e\n\u003cli\u003eKuper, H., \u0026amp; Wachter, K. (2023). Online violence against women journalists: A global snapshot. \u003cem\u003eJournalism Practice\u003c/em\u003e, 17(4), 735\u0026ndash;754. https://doi.org/10.1080/17512786.2022.2084882\u003c/li\u003e\n\u003cli\u003eLewis, R., Rowe, M., \u0026amp; Wiper, C. (2024). Technology-facilitated gender-based violence against politically active women: A systematic review of psychological and political consequences and women\u0026rsquo;s coping behaviors. \u003cem\u003eTrauma, Violence, \u0026amp; Abuse\u003c/em\u003e. Advance online publication. https://doi.org/10.1177/15248380231189000\u003c/li\u003e\n\u003cli\u003eLewis, R., Sharp, E., Remnant, J., \u0026amp; Redpath, R. (2017). \u0026lsquo;Safe spaces\u0026rsquo;: Experiences of feminist women-only space. \u003cem\u003eSociological Research Online\u003c/em\u003e, 20(4), 105\u0026ndash;118. https://doi.org/10.5153/sro.3915\u003c/li\u003e\n\u003cli\u003eMalanga, W. A. (2021). Cyber violence against women in rural Malawi: Forms, motivations, and coping strategies. arXiv preprint. https://arxiv.org/abs/2108.09806\u003c/li\u003e\n\u003cli\u003eMarwick, A. E., Blackwell, L., \u0026amp; Lo, K. (2021). \u0026ldquo;Yet another Twitter sh*tstorm\u0026rdquo;: Contentious communication and the normative dynamics of social media crowds. American Behavioral Scientist, 65(3), 321\u0026ndash;345. https://doi.org/10.1177/0002764221989781\u003c/li\u003e\n\u003cli\u003eMendes, K., Ringrose, J., \u0026amp; Keller, J. (2018). #MeToo and the promise and pitfalls of challenging rape culture through digital feminist activism. \u003cem\u003eEuropean Journal of Women\u0026rsquo;s Studies\u003c/em\u003e, 25(2), 236\u0026ndash;246. https://doi.org/10.1177/1350506818765318\u003c/li\u003e\n\u003cli\u003eMitchell, K. J. (2025). Measuring technology-facilitated violence: Methodological challenges and directions for research. Violence Against Women, 31(2), 145\u0026ndash;167. https://doi.org/10.1177/10778012231123456\u003c/li\u003e\n\u003cli\u003eMonni, S. S., \u0026amp; Sultana, A. (2016). Investigating cyber bullying: Pervasiveness, causes and socio-psychological impact on adolescent girls. Journal of Public Administration and Governance, 6(4), 12\u0026ndash;24. https://doi.org/10.5296/jpag.v6i4.10132\u003c/li\u003e\n\u003cli\u003eMridha, M. R., Ashrafuzzaman, S. M., \u0026amp; Sara, S. S. (2024). Uncovering the prevalence and consequences of cyberbullying among female students as virtual violence in Bangladesh. International Journal of Social Science Research and Review, 7(7), 46\u0026ndash;57. https://doi.org/10.47814/ijssrr.v7i7.2167\u003c/li\u003e\n\u003cli\u003eMukred, M. (2024). The roots of digital aggression: Exploring cyber-violence against women. Online Safety Journal, 10(2), 45\u0026ndash;59. https://doi.org/10.1016/j.safe.2024.05.001\u003c/li\u003e\n\u003cli\u003eOrnstein, M. D., McLeod, C. B., \u0026amp; Smith, P. M. (2023). The psychosocial impacts of intimate partner violence against women in LMIC contexts: Toward a holistic approach. \u003cem\u003eTrauma, Violence, \u0026amp; Abuse\u003c/em\u003e, 24(5), 1315\u0026ndash;1329. https://doi.org/10.1177/15248380221103252\u003c/li\u003e\n\u003cli\u003ePalinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., \u0026amp; Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533\u0026ndash;544. https://doi.org/10.1007/s10488-013-0528-y\u003c/li\u003e\n\u003cli\u003eParadiso, M., Santoro, G., \u0026amp; Di Marco, S. (2024). Online misogyny and its consequences: A systematic review. Aggression and Violent Behavior, 75, 101945. https://doi.org/10.1016/j.avb.2023.101945\u003c/li\u003e\n\u003cli\u003ePolyzoidou, V. (2024). Digital violence against women: Is there a real need for discussion? International Journal for the Semiotics of Law, 37, 123\u0026ndash;142. https://doi.org/10.1007/s11196-024-10179-3\u003c/li\u003e\n\u003cli\u003ePosetti, J., Shabbir, N., Maynard, D., Bontcheva, K., \u0026amp; Aboulez, N. (2022). The chilling: A global study of online violence against women journalists. \u003cem\u003eInternational Journal of Press/Politics\u003c/em\u003e, 27(4), 927\u0026ndash;954. https://doi.org/10.1177/19401612221108144\u003c/li\u003e\n\u003cli\u003ePowell, A., \u0026amp; Henry, N. (2017). Sexual violence in a digital age. Palgrave Macmillan.\u003c/li\u003e\n\u003cli\u003eRahman, M. M. (2021). The Digital Security Act 2018 in Bangladesh: An assessment of its impact on freedom of expression. \u003cem\u003eAsian Journal of Comparative Law\u003c/em\u003e, 16(S1), S57\u0026ndash;S70. https://doi.org/10.1017/asjcl.2021.13\u003c/li\u003e\n\u003cli\u003eRahman, A., \u0026amp; Khan, N. (2021). Digital misogyny in Bangladesh: Exploring the intersection of culture and technology. Journal of South Asian Media Studies, 15(3), 201\u0026ndash;218. https://doi.org/10.1080/17513057.2021.1990412\u003c/li\u003e\n\u003cli\u003eRegmi, P. R., Waithaka, E., Paudyal, A., Simkhada, P., \u0026amp; Van Teijlingen, E. (2016). Guide to the design and application of online questionnaires. Journal of Clinical and Diagnostic Research, 10(6), ZC01\u0026ndash;ZC04. https://doi.org/10.7860/JCDR/2016/17824.7901\u003c/li\u003e\n\u003cli\u003eReed, L. A., Tolman, R. M., \u0026amp; Ward, L. M. (2019). Gender matters: Experiences of cyber sexual harassment among adolescent girls. \u003cem\u003eJournal of Adolescent Research\u003c/em\u003e, 34(5), 533\u0026ndash;559. https://doi.org/10.1177/0743558418792478\u003c/li\u003e\n\u003cli\u003eReed, L. A., Tolman, R. M., \u0026amp; Ward, L. M. (2022). Cyber sexual harassment and psychological distress among young adult women. \u003cem\u003eJournal of Interpersonal Violence\u003c/em\u003e, 37(15\u0026ndash;16), NP13345\u0026ndash;NP13368. https://doi.org/10.1177/08862605211015291\u003c/li\u003e\n\u003cli\u003eSalem, M. R., Albaghli, H., \u0026amp; Alasmary, W. (2023). Anonymity and response validity in online surveys: Evidence from sensitive-topic research. BMC Public Health, 23(1), 1456. https://doi.org/10.1186/s12889-023-14567-3\u003c/li\u003e\n\u003cli\u003eSardinha, L., Maheu-Giroux, M., St\u0026ouml;ckl, H., Meyer, S. R., \u0026amp; Garc\u0026iacute;a-Moreno, C. (2022). Global, regional, and national prevalence estimates of physical or sexual, or both, intimate partner violence against women in 2018. \u003cem\u003eThe Lancet\u003c/em\u003e, 399(10327), 803\u0026ndash;813. https://doi.org/10.1016/S0140-6736(21)02664-7\u003c/li\u003e\n\u003cli\u003eSharpe, D. (2015). Chi-square test is statistically significant: Now what? Practical Assessment, Research, and Evaluation, 20(1), 8. https://doi.org/10.7275/tbfa-x148\u003c/li\u003e\n\u003cli\u003eSheikh, M. M. R., \u0026amp; Rogers, M. M. (2023). Technology-facilitated sexual violence and abuse in low and middle-income countries: A scoping review. Trauma, Violence, \u0026amp; Abuse, 25(2), 1614\u0026ndash;1629. https://doi.org/10.1177/15248380231191189\u003c/li\u003e\n\u003cli\u003eSheikh, M. M. R., Hossan, M. R., \u0026amp; Menih, H. (2023). Cyberbullying victimization and perpetration among university students in Bangladesh: Prevalence, impact and help-seeking practices. Journal of School Violence, 22(2), 198\u0026ndash;214. https://doi.org/10.1080/15388220.2023.2168681\u003c/li\u003e\n\u003cli\u003eSt\u0026ouml;ckl, H. (2024). Violence against women as a public health issue. Annual Review of Public Health, 45(1), 341\u0026ndash;360. https://doi.org/10.1146/annurev-publhealth-060722-025138\u003c/li\u003e\n\u003cli\u003eSultana, A., \u0026amp; Islam, N. (2022). Online gender-based violence in Bangladesh: A study on university students. \u003cem\u003eJournal of Social Studies\u003c/em\u003e, 158, 45\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eSuzor, N. P., Dragiewicz, M., Harris, B., Gillett, R., Burgess, J., \u0026amp; Van Geelen, T. (2019). Human rights by design: The responsibilities of social media platforms to address gender-based violence online. \u003cem\u003ePolicy \u0026amp; Internet\u003c/em\u003e, 11(1), 84\u0026ndash;103. https://doi.org/10.1002/poi3.185\u003c/li\u003e\n\u003cli\u003eUdo, G. G., \u0026amp; Bagchi, K. K. (2020). USING PERSONAL NORM MODEL TO EXPLAIN CYBER-HARASSMENT INTENTION AND BEHAVIOR. Issues in Information Systems, 21(4).\u003c/li\u003e\n\u003cli\u003eUllman, S. E., et al. (2025). Global interventions to prevent violence against women and girls: A systematic review of the RESPECT Women framework. The Lancet Global Health, 13(2), e152\u0026ndash;e165. https://doi.org/10.1016/S2214-109X(24)00556-4\u003c/li\u003e\n\u003cli\u003eUnited Nations Regional Office for Arab States \u0026amp; UN Women. (2021). Online violence against women in the Arab States region. United Nations. https://pmc.ncbi.nlm.nih.gov/articles/PMC10913330/\u003c/li\u003e\n\u003cli\u003eUnited Nations Regional Information Centre. (2024, January 18). Cyberviolence against women and girls: The growing threat of the digital age. United Nations. https://unric.org/en/cyberviolence-against-women-and-girls-the-growing-threat-of-the-digital-age/\u003c/li\u003e\n\u003cli\u003eUN Women. (2021). Online and ICT-facilitated violence against women and girls during COVID-19. United Nations. https://www.unwomen.org/en/digital-library/publications/2021/07/online-and-ict-facilitated-violence-against-women-and-girls-during-covid-19\u003c/li\u003e\n\u003cli\u003eUN Women. (2023). \u003cem\u003eFacts and figures: Ending violence against women\u003c/em\u003e. https://www.unwomen.org/en/what-we-do/ending-violence-against-women/facts-and-figures\u003c/li\u003e\n\u003cli\u003eVandenbosch, L., \u0026amp; van Oosten, J. M. F. (2017). The relationship between online victimization and psychological outcomes: A meta-analysis. Journal of Youth and Adolescence, 46(6), 1343\u0026ndash;1361. https://doi.org/10.1007/s10964-017-0672-x\u003c/li\u003e\n\u003cli\u003eVickery, J. R., \u0026amp; Everbach, T. (Eds.). (2018). \u003cem\u003eMediating misogyny: Gender, technology, and harassment\u003c/em\u003e. Palgrave Macmillan. https://doi.org/10.1007/978-3-319-72917-6\u003c/li\u003e\n\u003cli\u003eWessells, M. G., \u0026amp; Kostelny, K. (2022). The Psychosocial Impacts of Intimate Partner Violence against Women in LMIC Contexts: Toward a Holistic Approach. International Journal of Environmental Research and Public Health, 19(21), 14488. https://doi.org/10.3390/ijerph192114488\u003c/li\u003e\n\u003cli\u003eWoodlock, D. (2023). Technology-facilitated stalking: Expanding the research and response landscape. Current Issues in Criminal Justice, 34(2), 135\u0026ndash;152. https://doi.org/10.1080/10345329.2022.2159528\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2021). Violence against women prevalence estimates, 2018: Global, regional and national prevalence estimates for intimate partner violence against women and global and regional prevalence estimates for non-partner sexual violence against women. WHO. https://www.who.int/publications/i/item/9789240022256\u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eWorsley, J. D., McIntyre, J. C., Corcoran, R., \u0026amp; Bentall, R. P. (2023). Childhood maltreatment and problematic social media use: The mediating role of attachment and mental health. Computers in Human Behavior, 138, Article 107479. https://doi.org/10.1016/j.chb.2022.107479\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1: Prevalence of Online Violence Against Young\u0026ensp;Women (n=202)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"545\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eOffensive Comment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eDoxing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eImpersonation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e38.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eHate Speech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eSextortion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eStalking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2: Chi-Square Test of Association at Location,\u0026ensp;Internet Use and Occupations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternet usage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eRural\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eUrban\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eStudent\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eEmployed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eFrequencies of violence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e49.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e65.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eOffensive\u003c/p\u003e\n \u003cp\u003eComment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e10.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e12.633a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eDoxing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e7.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eImpersonation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e24.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e25.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e33.601a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eHate Speech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e27.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e9.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e83.296a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eStalking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e10.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.936a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 3: Most consequences\u0026ensp;of online violence (n=202)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eFre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eTrust decreased on other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.2079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.40683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003ePhysical Related Stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.0594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.23697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eMental Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e61.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.6139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.48807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eWithdrawal from online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.1188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.32437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eAvoidance of Participating in public\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eBehavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eTable 4: differences in health consequences based on internet use, locations, and occupations\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"549\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003eInternet usage\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cem\u003eLocations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cem\u003eOccupations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cem\u003eRural\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cem\u003eStudent\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eTrust decreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e31.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e7.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e17.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003ePhysical related\u003c/p\u003e\n \u003cp\u003eStress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e10.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e16.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eMental_depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e58.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e47.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e26.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eWithdrawal from\u003c/p\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e16.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e16.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e3.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eBehavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e26.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e15.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e24.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eAvoidance of\u003c/p\u003e\n \u003cp\u003eparticipation in public space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e1.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e2.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e5.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 5\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: \u003cstrong\u003eTable 5: Relations Between Online Violence Types and Their Effects on\u0026ensp;Young Women\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOffensive\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eComment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDoxing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImpersonation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHate Speech\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSextortion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStalking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eTrust decreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e4.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003ePhysical related\u003c/p\u003e\n \u003cp\u003eStress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e1.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e4.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eMental depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e1.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e22.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e9.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e3.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eWithdrawal from\u003c/p\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e1.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e9.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e1.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e1.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e2.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eBehavioral issues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e13.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e5.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e26.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e13.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e9.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e5.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eAvoidance of\u003c/p\u003e\n \u003cp\u003eparticipation in public space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e6.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e6.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Online Violence Against Women (OVAW), Digital Gender-Based Violence, Bangladesh, Patriarchy, Cyber Harassment, Psychological Impact, Digital Literacy","lastPublishedDoi":"10.21203/rs.3.rs-8364676/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8364676/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDigital connectivity has spread rapidly to provide new access to empowering women in education, employment, and civil engagement while concurrently placing them under increased risks of violence against women (VAW) online. While Online Violence against Women (OVAW) has often been noted globally as a non-communicable neglected public health problem, empirical knowledge for Bangladesh is sparse, disjointed, and mostly on students. This research employs an integrated General Strain\u0026ndash;Intersectional Feminism theory as its theoretical framework, extending global OVAW theory by revealing how patriarchal honor rules in Bangladesh exacerbate online abuse into distinctly severe forms of reputational strain. To address these gaps, an exploratory sequential mixed-methods studies were applied to synthesize semi-structured interviews with a cross-sectional online questionnaire (N\u0026thinsp;=\u0026thinsp;202) that was conducted from July to August 2024. The statistical analyses also reveal significant links between the prevalence and severity of OVAW and women\u0026rsquo;s internet use intensity, place of residence, and employment status. Women living in rural areas and those engaged in paid work were found to be disproportionately affected. By distinguishing among different forms of online abuse and tracing their varied psychological, behavioral, and physical impacts, this study offers new insight into the scope and consequences of OVAW in low- and middle-income settings. Findings indicate that impersonation and hate speech were most frequent modes of OVAW, with nearly two out of every five young women being subjected to impersonation and more than a quarter being subjected to hate speech. Moreover, psychological harms were most prominent: depression and social withdrawal being reported by more than 60% of respondents, with numerous respondents also reporting erosion of trust in others. These findings suggest that OVAW in Bangladesh is not simply an extension of global trends but is sustained by entrenched patriarchal norms, persistent stigma, and limited institutional safeguards. It calls for comprehensive policy measures combining digital literacy initiatives, accessible mental health services, and stronger accountability frameworks to foster a safer and more equitable digital environment for women.\u003c/p\u003e","manuscriptTitle":"Patterns of online impersonation violence and mental health consequences among women in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 16:43:52","doi":"10.21203/rs.3.rs-8364676/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79c797a5-47d6-47d6-86fb-8a7774c50ef1","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T21:38:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 16:43:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8364676","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8364676","identity":"rs-8364676","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}