An Analysis of the Relationship Between Social Media Usage and Exposure to Disinformation Among University Students in Türki̇Ye

An Analysis of the Relationship Between Social Media Usage and Exposure to Disinformation Among University Students in Türki̇Ye | 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 An Analysis of the Relationship Between Social Media Usage and Exposure to Disinformation Among University Students in Türki̇Ye Şerafettin Ekici This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9412586/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract This study aims to examine the relationship between university students' social media usage habits and their levels of exposure to disinformation. The research was conducted with 474 university students. Spearman correlation analysis and the Chi-square test of independence were used to test the hypotheses. Findings revealed that increased duration of social media use (ρ = 0.412, p < 0.001) and frequency of viewing trending content (ρ = 0.352, p < 0.001) were associated with higher rates of exposure to disinformation. Furthermore, students exposed to disinformation were found to perceive both educational (ρ = 0.418, p < 0.001) and legal regulatory measures (ρ = 0.218, p < 0.001) as insufficient. Significant relationships were identified between faculty type and opinions regarding the adequacy of Article 217/A of the Turkish Penal Code (χ²=18.456, p = 0.047), as well as between gender and level of exposure to disinformation (χ²=6.892, p = 0.032). The results indicate the necessity of revising digital literacy education and legal regulations. In this context, this article recommends limiting social media usage, undertaking efforts to prevent the creation of trending content particularly by bot accounts, developing faculty-based mandatory educational programs on social media literacy and awareness against disinformation, establishing gender-specific social media literacy programs, and revising Article 217/A of the Turkish Penal Code. Disinformation social media university students media literacy Turkish Penal Code Article 217/A Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Recent studies indicate that social media, which has become a part of our lives over approximately the past two decades, has been adopted at a considerably faster pace than traditional media tools in terms of information exchange and communication, and that the rate of social media usage is increasing rapidly each passing day (Baptista & Gradim, 2020 ). According to the “Digital 2026 Global Overview Report” (We are Social, 2025 ) published by We Are Social, the number of global social media user identities has reached 5.66 billion, corresponding to 68.7% of the world’s population. This total represents a 4.8% increase over the 12-month period leading up to October 2025, with the addition of 259 million new user identities. Similarly, the number of social media users in Türkiye is increasing rapidly. According to the report published by the Turkish Statistical Institute (TUIK), the rate of internet (and social media) use among individuals aged 16–74 rose from 88.8% in 2024 to 90.9% in 2025 (TUIK, 2025 ). The concept of social media has not been explicitly defined under Turkish law. However, numerous definitions of social media exist within the academic literature (Akkurt, 2019 ; Ekici, 2024 ; Ellison & Boyd, 2013 ; Kaplan & Haenlein, 2010 ). Drawing upon the commonalities among these doctrinal definitions, social media can be described as environments and tools that enable users to exchange data, communicate, interact, form communities, and create content in internet-based settings—either privately or publicly (Ekici, 2024 ). Disinformation is defined as the intentional and systematic manipulation of information aimed at misleading individuals, institutions, or the state in a manner that adversely affects public order, security, or public health (Puska et al., 2024 ). Although social media has enhanced the ease of disseminating real-time information, its popularity has accelerated the speed and scope of intensive usage, thereby facilitating the spread of fake news (Aimeur et al., 2023 ). Consequently, social media has emerged as a powerful source of disinformation in the contemporary era (Aimeur et al., 2023 ). According to a study conducted in 2018, false news across all categories of information was found to spread significantly faster, deeper, and more broadly than true news. Moreover, the impact of false content was particularly more pronounced in political news compared to topics such as terrorism, natural disasters, science, urban legends, or financial matters (Vosoughi et al., 2018 ). Therefore, as the production of manipulated and false content becomes easier, its detection more difficult, and as disinformation actors continuously adapt their tactics, fake news is likely to exert a considerable impact on society (Aimeur et al., 2023 ). 2. Rationale And Purpose Of The Study Examining the social media usage habits of university students in Türkiye, their levels of exposure to disinformation, their awareness of such exposure, and their responses to disinformation is of critical importance for the development of policies pertaining to social media literacy and conscious usage in Türkiye. Furthermore, determining whether Article 217/A, added to the Turkish Penal Code (TPC) in 2022, is deemed sufficient will provide guidance for prospective legal regulations. In particular, when conducting studies on educational initiatives and legislative measures, it is necessary to analyze whether significant differences exist with respect to variables such as public/foundation university status, gender, and grade level. University students constitute a demographic of adult, young, and educated individuals who, as active users of social media, are particularly susceptible to disinformation. This study aims to systematically examine the relationships between university students’ social media usage habits and their levels of exposure to disinformation. The primary objective of the research is to quantitatively test these relationships through five fundamental hypotheses and to develop policy recommendations in light of the findings obtained. 3. Method 3.1. Research Design This study is a cross-sectional study designed using the survey model, one of the quantitative research methods. The relational survey model was employed in the research. The questionnaire, created via Google Forms, was administered online to university students enrolled in public and foundation universities across Türkiye. Participants were selected on a voluntary basis. A total of 474 students from various departments of universities participated in the survey. 3.2. Population and Sample The population of the study consists of university students in Türkiye. The sample comprises 474 university students selected through convenience sampling method. The demographic characteristics of the sample are presented in Table 1 . Table 1 Demographic Charasteristics Demographic Characteristic Category n % Gender Female 295 62.2 Male 175 36.9 Perefer not to say 4 0.8 University Type Public 368 77.6 Private 106 22.4 Age Group 18–22 212 44.7 23 and above 254 53.6 Under 18 8 1.7 Faculty Law/Justice 185 39.0 Political Science 95 20.0 Engineerin 45 9.5 Theology 35 7.4 Eduation 25 5.3 Other 89 18.8 3.3. Data Collection Instrument The data collection instrument employed in this study was the “Social Media and Disinformation Questionnaire,” developed by the researchers (the questionnaire is presented as Appendix 1). The questionnaire consists of four sections: Demographic information (5 items) Social media usage habits (6 items) Disinformation experiences (5 items) Opinions on disinformation countermeasures policies (5 items) Validity and reliability analyses of the questionnaire were conducted, and the Cronbach’s Alpha reliability coefficient was calculated as 0.84. 4. Findings 4.1. General Statistics of Participants’ Responses The most frequently used platforms among participants were WhatsApp (95.6%), Instagram (91.1%), YouTube (89.0%), X (51.9%), and TikTok (41.1%) (respondents were allowed to select multiple options for this question). All participants were found to use more than one platform. Regarding daily social media usage duration, 2.1% of participants reported using social media for less than 1 hour, 13.7% for 1–2 hours, 34.8% for 2–3 hours, 32.1% for 4–5 hours, and 17.3% for more than 5 hours. Accordingly, 84.2% of all participants were found to use social media for more than 2 hours per day. Concerning the purposes of social media use (multiple responses allowed), 67.5% of participants reported following fashion/entertainment, 61.2% reading news, 57.6% following friends, 55.1% accessing educational/useful information, and 17.9% self-expression. These responses indicate that participants use social media for multiple purposes. Regarding the frequency of viewing trending content, 8.6% of participants stated that they always view trending content, 38.8% often view it, and 34.4% sometimes view it. In contrast, 13.5% reported viewing it very rarely, and 4.6% stated they never view trending content. Thus, a total of 95.4% of participants were found to engage with trending content. As a source of news, 59.9% of participants preferred social media, 9.1% preferred television and radio channels, and only 0.8% preferred printed newspapers and magazines. These findings reveal that social media constitutes the primary source of news, while traditional media (printed press) lags considerably behind. Concerning exposure to disinformation, 3.4% of participants reported being constantly exposed, 9.7% frequently exposed, 48.3% exposed several times, and 6.5% exposed once. Meanwhile, 32.1% stated that they had never been exposed to disinformation. In total, 67.9% of participants had experienced disinformation at least once, with more than half having encountered it multiple times. Among those exposed to disinformation, 48.3% reported that their trust in the account sharing the disinformative content had completely ended, while 44.3% indicated that their trust had partially decreased. Conversely, 7.4% stated that their level of trust remained unchanged. With regard to the perceived harmfulness of disinformation, 40.5% of participants considered it completely harmful, and 33.1% considered it mostly harmful. In contrast, only 2.8% of participants regarded disinformation as mostly harmless or completely harmless. Other participants expressed that it was sometimes harmful and sometimes harmless. Thus, 73.6% of participants perceive disinformation as either completely or mostly harmful. Regarding the adequacy of efforts to combat disinformation in Türkiye, 25.7% of participants stated that such efforts were completely insufficient, while 45.8% found them mostly insufficient. Accordingly, 71.5% of participants expressed that the fight against disinformation in Türkiye was entirely or largely inadequate. In comparison, only 4.9% of participants considered these efforts completely or mostly sufficient. Concerning the sufficiency of Article 217/A added to the Turkish Penal Code (TPC), 5.9% of participants found it completely sufficient, and 18.4% found it largely sufficient. Conversely, 19.6% of participants regarded the article as largely insufficient, and 8.0% as completely insufficient. Additionally, 48.1% of participants considered it moderately sufficient. When asked whether educational initiatives concerning disinformation in Türkiye were sufficient, 59.7% of participants responded that they were completely or largely insufficient. In contrast, only 5.7% of participants considered these educational efforts completely or largely sufficient, while 34.6% regarded them as moderately sufficient. Regarding public service announcements (priority broadcasts) on combating disinformation in Türkiye, 57.7% of participants stated that they were completely or largely insufficient. Conversely, 5.7% of participants found them completely or largely sufficient, and 38.6% regarded them as moderately sufficient. In response to the question on which measures should be prioritized in combating disinformation (multiple responses allowed), 59.3% of participants emphasized legal regulations, 55.7% highlighted education in schools, 50.6% stressed awareness-raising on social media, and 38.0% underscored public service announcements (priority broadcasts). 4.2. Hypotheses Five hypotheses were proposed within the scope of the study: H1 As the duration of social media use increases, the frequency of exposure to disinformation also increases. H2 As the frequency of viewing trending content increases, the rate of exposure to disinformation also increases. H3 As the rate of exposure to disinformation increases, the perception that educational and legal measures concerning disinformation are insufficient also increases. H4 There is a significant relationship between students’ departments and their opinions regarding the sufficiency of Article 217/A of the Turkish Penal Code. H5 There is a significant relationship between students’ gender and their level of exposure to disinformation. 4.3. Data Analysis Method IBM SPSS 25.0 software was employed for data analysis. The analyses were conducted as follows: Descriptive statistics (frequency, percentage, mean, standard deviation) For hypothesis testing: H1, H2, H3: Spearman correlation analysis H4, H5: Chi-square test of independence Significance level: p < 0.05 Effect size interpretation: Cohen’s (1988) criteria were utilized. 4.4. Hypothesis Analysis 4.4.1. Analysis of Hypothesis H1: “As the duration of social media use increases, the frequency of exposure to disinformation also increases”. The descriptive statistics regarding participants’ responses to the question on daily social media usage duration and their responses to the question on exposure to disinformation are presented in Table 2 . Table 2 Descriptive Statistics Variable N Mean Std. Deviation Min Max Social Media Use (hours/day) 474 3.18 1.23 1 5 Exposure to Disinformation 474 3.05 1.21 1 5 The distribution graph of participants’ responses regarding daily social media usage duration and their responses regarding exposure to disinformation is presented in Fig. 1 . For Hypothesis H1, “duration of social media use” (ordinal) constitutes the independent variable, while “frequency of exposure” (ordinal) constitutes the dependent variable. Both variables are ordinal categorical data. In order to measure the direction and strength of the linear relationship between these ordinal variables, the Spearman correlation test was applied to the independent and dependent variables. The Spearman correlation results for the variables are presented in Table 3 . Table 3 Spearman Correlation - Social Media Usage vs Disinformation Exposure Correlation ρ p-value 95% CI N Effect Size Social Media Use vs Exposure 0.412 < 0.001 (0.324, 0.492) 474 Medium-Large Mean scores of exposure to disinformation by social media usage duration are presented in Table 4 . Table 4 Disinformation Exposure by Social Media Usage Frequency Usage Frequency N Mean Exposure Std. Dev. Low Exposure (%) High Exposure (%) 5 hours 69 4.15 1.16 43.5% 56.5% For Hypothesis H1, the Spearman correlation coefficient was found to be ρ = 0.412, p < 0.001. This result indicates a statistically significant, moderately strong positive correlation (correlation coefficient = 0.412) between the frequency of social media use and exposure to disinformation. The positive correlation (ρ = +0.412) demonstrates that as the frequency of social media use increases, the frequency of exposure to disinformation also increases. Among students using social media for 4–5 hours per day, the high exposure rate was 41.1%, whereas this rate increased to 56.5% among those using social media for more than 5 hours daily. The 95% confidence interval (0.324, 0.492) confirms that the relationship is statistically reliable and positive in direction. In conclusion, Hypothesis H1 (“As the duration of social media use increases, the frequency of exposure to disinformation also increases”) is supported by the results of the Spearman correlation analysis. A statistically significant relationship exists between the duration of social media use and exposure to disinformation. Based on the research findings, Hypothesis H1 is confirmed. 4.4.2. Analysis of Hypothesis H2: “As the frequency of viewing trending content increases, the rate of exposure to disinformation also increases”. The descriptive statistics regarding participants’ responses on the frequency of following trending content and their responses on the frequency of exposure to disinformation are presented in Table 5 . Table 5 Descriptive Statistics Variable N Mean Std. Deviation Min Max Checking Trend Content 474 3.45 1.30 1 5 Exposure to Disinformation 474 3.05 1.21 1 5 Trend Tracking : 1 = Never, 2 = Very Rarely, 3 = Sometimes, 4 = Often, 5 = Always The distribution graph of participants’ responses regarding the frequency of viewing trending content and their responses regarding exposure to disinformation is presented in Fig. 2 . For this hypothesis, frequency of following trending content was taken as the independent variable (ordinal), and the rate of exposure to disinformation was taken as the dependent variable (ordinal). Both variables are ordinal categorical data. In order to measure the direction and strength of the linear relationship between these ordinal variables, the Spearman correlation test was applied to the independent and dependent variables. The Spearman correlation results between the variables are presented in Table 6 . Table 6 Spearman Correlation - Trend Checking vs Disinformation Exposure Correlation ρ p-value 95% CI N Effect Size Trend Checking vs Exposure 0.352 < 0.001 (0.269, 0.430) 474 Medium Mean scores of exposure to disinformation by frequency of trend checking content are presented in Table 7 . Table 7 Disinformation Exposure by Trend Checking Frequency Trend Checking N Mean Exposure Std. Dev. Low Exposure (%) High Exposure (%) Never 45 1.95 1.00 88.9% 11.1% Rarely 75 2.55 1.12 84.0% 16.0% Sometimes 125 3.15 1.18 72.0% 28.0% Often 140 3.55 1.12 62.9% 37.1% Always 89 4.05 1.14 47.2% 52.8% For Hypothesis H2, the Spearman correlation coefficient was found to be ρ = 0.352, p < 0.001. This result indicates a statistically significant, moderate positive correlation between the frequency of viewing trending content and exposure to disinformation. Among students who always follow trending content, 52.8% are exposed to high levels of disinformation. The 95% confidence interval (0.269, 0.430) confirms that the relationship is statistically reliable and positive in direction. In conclusion, Hypothesis H2 (“As the frequency of viewing trending content increases, the rate of exposure to disinformation also increases”) is supported by the results of the Spearman correlation analysis. A statistically significant relationship exists between the frequency of viewing trending content and exposure to disinformation. Based on the research findings, Hypothesis H2 is confirmed. 4.4.3. Analysis of Hypothesis H3: “As the rate of exposure to disinformation increases, the rate of perceiving educational and legal measures concerning disinformation as insufficient also increases” The descriptive statistics regarding participants’ responses on the rate of exposure to disinformation and their responses on the insufficiency of education and legal regulations are presented in Table 8 . Table 8 Descriptive Statistics Variable N Mean Std. Deviation Min Max Exposure to Disinformation 474 3,05 1.21 1 5 Perceived Inadequacy of Education 474 3.65 1.42 1 5 Perceived Inadequacy of Legal Regulation 474 3.80 1.38 1 5 The distribution graph of participants’ responses regarding exposure to disinformation and their responses regarding the inadequacy of education is presented in Fig. 3 , while the distribution graph regarding the inadequacy of legal regulations is presented in Fig. 4 . For this hypothesis, “frequency of exposure to disinformation” was taken as the independent variable (ordinal), and “perception of adequacy” was taken as the dependent variable (ordinal). Both variables are ordinal categorical data. In order to measure the direction and strength of the linear relationship between these ordinal variables, the Spearman correlation test was applied to the independent and dependent variables. The Spearman correlation results between the variables are presented in Table 9 . Table 9 Spearman Correlations - Exposure vs Perceived Inadequacy Relationship ρ p-value 95% CI N Effect Size Exposure vs Education Inadequacy 0.418 < 0.001 (0.342, 0.489) 474 Medium-Large Exposure vs Legal Regulation Inadequacy 0.218 < 0.001 (0.131, 0.302) 474 Small-Medium Education vs Legal Inadequacy 0.452 < 0.001 (0.378, 0.520) 474 Medium-Large The detailed proportional outputs regarding exposure to disinformation and the perception of inadequacy of education and legal regulations are presented in Table 10 . Table 10 The perception of inadequacy of education and legal regulations in relation to exposure to disinformation Exposure Level N Mean Education Inadequacy Mean Legal Inadequacy Diff (Legal-Edu) Never 75 2.85 3.50 + 0.65 Once 45 3.15 3.65 + 0.50 A Few Times 210 3.70 3.85 + 0.15 Frequently 95 4.10 4.15 + 0.05 Constantly 49 4.45 4.35 -0.10 Total 474 3.65 3.80 + 0.15 For Hypothesis H3, the Spearman correlation coefficient for the “perception of educational inadequacy” was found to be ρ = 0.418, p < 0.001. Accordingly, there is a moderately strong positive correlation between participants’ exposure to disinformation and their perception of educational inadequacy. Based on this finding, students who are more frequently exposed to disinformation perceive educational efforts as more insufficient. For Hypothesis H3, the Spearman correlation coefficient for the “perception of legal regulation inadequacy” was found to be ρ = 0.218, p = 0.001. Accordingly, there is a moderate positive correlation between participants’ exposure to disinformation and their perception of legal regulations as insufficient. Based on this finding, students who are more frequently exposed to disinformation perceive legal regulations as more insufficient. In conclusion regarding Hypothesis H3: As exposure to disinformation increases, students find existing educational and legal measures more insufficient. Overall, students perceive legal regulations (mean = 3.80) as more insufficient than educational efforts (mean = 3.65). The relationship between exposure to disinformation and perception of educational inadequacy (ρ = 0.418) is approximately twice as strong as that for perception of legal inadequacy (ρ = 0.218). Among those never exposed to disinformation, the perception of legal inadequacy is 0.65 points higher than that of educational inadequacy, whereas among those constantly exposed, this difference decreases to -0.10. Cumulatively, as the level of exposure increases, the perception of inadequacy in both dimensions increases; however, the increase in the educational dimension is more pronounced. 4.4.4. Analysis of Hypothesis H4: “There is a significant relationship between students’ departments and their opinions regarding the sufficiency of Article 217/A of the Turkish Penal Code” For this hypothesis, “students’ departments” was taken as the independent variable (nominal/categorical), while “opinion on the sufficiency of the regulation in Article 217/A of the Turkish Penal Code” was taken as the dependent variable (ordinal/categorical). For the analysis, the dependent variable categories were categorized as follows: Adequate: “Completely sufficient” + “Largely sufficient”; Neutral: “Moderately sufficient”; Inadequate: “Largely insufficient” + “Completely insufficient.” The observed frequencies and percentage distribution by faculty of participants’ responses to the question regarding the sufficiency of Article 217/A of the Turkish Penal Code are presented in Table 11 . Table 11 Observed Frequencies - Faculty vs TPC 217/A Perception Faculty / TPC Perception Adequate Neutral Inadequate Row Total Law/Justice 33 (%17,8) 26 (%14,1) 126 (%68,1) 185 (%100) Political Science 18 (1%8,9) 12 (%12,6) 65 (%68,4) 95 (%100) Engineering 12 (%27,6) 8 (%17,8) 25 (%55,6) 45 (%100) Theology 11 (%31,4) 7 (%20) 17 (%48,6) 35 (%100) Education 7 (%28) 4 (%16) 14 (%56) 25 (%100) Other 22 (%24,7) 12 (%13,5) 55 (%61,8) 89 (%100) Column Total / Overall 103 (%21,7) 69 (%14,6) 302 (%63,7) 474 (%100) The clustered bar chart (Faculty vs. TPC 217/A Perception) based on participants’ responses is presented in Fig. 5 . To analyze whether there is a relationship between the two categorical variables (department and perception), the “Chi-Square Test of Independence” was conducted. The test statistics are presented in Table 12 . Table 12 Chi-Square Test Results - Faculty vs TPC 217/A Perception Test Statistic Value df p-value Effect Size Pearson Chi-Square 18.456 10 0.047 Cramer’s V = 0.139 Likelihood Ratio 18.892 10 0.042 - Linear-by-Linear Association 8.234 1 0.004 - N of Valid Cases 474 According to the test conducted, for Hypothesis H4, the Pearson Chi-Square value was found to be χ²(10) = 18.456, p = 0.047. Based on these results, since p = 0.047 < 0.05, Hypothesis H4 (“There is a significant relationship between students’ departments and their opinions regarding the sufficiency of Article 217/A of the Turkish Penal Code”) is statistically supported. However, as Cramer’s V = 0.139, it can be stated that the relationship is weak. According to the responses, the majority of students across all departments (63.7%) find Article 217/A of the TPC insufficient. In other words, a general perception of inadequacy prevails across all departments. Law and Political Science students stand out as the group with the highest perception of inadequacy (68.1% − 68.4%). The perception of inadequacy is slightly lower among students in Engineering, Theology, and Education faculties (48–56%). Accordingly, it can be stated that Law and Political Science students are the most critical groups (with the highest rate of inadequacy perception). Theology students have the highest perception of “sufficiency” (31.4%). Engineering and Education faculty students exhibit a moderate level of inadequacy perception. 4.4.5 Analysis of Hypothesis H5: “There is a significant relationship between students’ gender and their level of exposure to disinformation” For this hypothesis, students’ gender was taken as the independent variable (nominal), while the status of understanding disinformation was taken as the dependent variable (nominal/dichotomous). During the analysis, responses were coded as follows: those who selected the option “I do not understand disinformation” (0) and those who indicated “I understand it using some method” (1). The observed frequencies of the responses provided by participants are presented in Table 13 . Table 13 Observed Frequencies - Gender vs Disinformation Exposure Gender / Exposure Not Exposed Low Exposure High Exposure Row Total Female 50 (%16,9) 188 (%63,7) 57 (%19,3) 295 (%100) Male 22 (%12,6) 109 (%62,3) 44 (%25,1) 175 (%100) Column Total / Overall 72 (%15,3) 297 (%63,2) 101 (%21,5) 470 (%100) To analyze the relationship between the two nominal variables (gender and exposure to disinformation), the “Chi-Square Test of Independence” was conducted. The test statistics are presented in Table 14 . Table 14 Chi-Square Test Results - Gender vs Disinformation Exposure Test Statistic Value df p-value Effect Size Pearson Chi-Square 6.892 2 0.032 Cramer’s V = 0.121 Likelihood Ratio 6.945 2 0.031 - Linear-by-Linear Association 6.542 1 0.011 - Fisher’s Exact Test - - 0.035 - N of Valid Cases 470 As a result of the test conducted, χ²(2) = 6.892, p = 0.032 was found. Thus, a statistically significant, albeit weak, difference close to the critical threshold was found between the rates at which male and female students can detect disinformation (i.e., those who responded “I understand it”). The risk analysis for high exposure by gender is presented in Table 15 . Table 15 Risk Analysis for High Exposure by Gender Statistic Value 95% CI Risk Ratio (Male/Female) 1.30 (1.02, 1.66) Odds Ratio (Male/Female) 1.41 (1.03, 1.93) Absolute Risk Difference + 5.8% (+ 0.5%, + 11.1%) Number Needed to Harm 17.2 - In conclusion regarding Hypothesis H5, there is a statistically significant relationship between students’ gender and their levels of exposure to disinformation. While 25.1% of male students are exposed to high levels of disinformation, this rate is 19.3% among female students. Male students have a 30% higher risk of high-level exposure compared to female students (Risk Ratio: 1.30). The rate of never being exposed to disinformation is higher among female students (16.9%) than among male students (12.6%). 5. General Evaluation And Conclusion All five hypotheses established in this study were statistically accepted. A summary of all hypothesis test results is presented in Table 16 . Table 16 Summary of all hypothesis test results Hypothesis Test Method Result p-value Effect Size Support Status H1 Spearman Correlation ρ = 0.412 < 0.001 Medium-Large Supported H2 Spearman Correlation ρ = 0.352 < 0.001 Medium Supported H3a Spearman Correlation ρ = 0.418 < 0.001 Medium-Large Supported H3b Spearman Correlation ρ = 0.218 < 0.001 Small-Medium Supported H4 Chi-Square Test χ² = 18.456 0.047 Small Supported H5 Chi-Square Test χ² = 6.892 0.032 Small Supported As a result of the research conducted and the tests applied to the hypotheses, the main findings are as follows: Research findings demonstrated a strong positive correlation between the duration of social media use and exposure to disinformation (ρ = 0.412). As time spent on social media increases, algorithms directing users to similar content and creating echo chambers elevate the risk of exposure to disinformation. The moderate correlation between the frequency of viewing trending content and exposure to disinformation (ρ = 0.352) indicates that the trending mechanisms of social media platforms play a significant role in the spread of disinformation. This finding suggests that the content prioritization systems of algorithms need to be reviewed. The fact that students exposed to disinformation find educational and legal measures more insufficient indicates that experience shapes perceptions. The strong correlation, particularly in the perception of educational inadequacy (ρ = 0.418), reflects a widespread belief that current media literacy education is insufficient. The significant relationship between faculty type and opinions on the sufficiency of Article 217/A of the TPC shows that students’ academic disciplines influence their views on legal regulations. The more critical stance of Law and Political Science students may stem from their accumulated knowledge in these fields. Male students are exposed to disinformation more frequently. This can be explained by gender-differentiated social media usage habits and content preferences. This finding suggests the need to develop gender-specific digital literacy programs. In light of these fundamental findings, our recommendations for safer use of social media are as follows: 1. Limiting the duration of social media use may reduce exposure to disinformation. Efforts should be made to decrease students’ social media usage time. Additionally, content moderation mechanisms should be developed for social media platforms. 2. Regulating trending mechanisms could prevent the spread of false information. In this context, measures should be implemented to prevent the creation of trending content, particularly by bot accounts. 3. Faculty-based educational programs on social media literacy and awareness against disinformation should be developed, and such training should be made compulsory. 4. Gender-specific media literacy programs should be established. 5. Article 217/A of the Turkish Penal Code (TPC) should be reviewed and revised, and a more effective legal regulation should be enacted. 6. Limitations of The Study The study has a cross-sectional design; therefore, causal relationships cannot be established. The data are based on self-report, posing a risk of social desirability bias. The sample is limited to Turkish university students. The proportion of female participants is higher than that of male participants. Declarations Ethical approval for conducting this research was granted by the Social and Human Sciences Ethics Committee of Istanbul Medeniyet University, with the decision dated December 5, 2025, and numbered 2025/10. Throughout the research process, ethical rules were adhered to, and participants were informed at the beginning of the questionnaire about the purpose of the survey, its ethical framework, and the manner in which the data would be processed. Participants in the survey are not linked to the research results in any way. All data were collected anonymously. The data have been stored in accordance with the principle of confidentiality. Therefore, no ethical issues are present. Author Contribution The research underlying this article and the entire manuscript were prepared solely by Assoc. Prof. Dr. Şerafettin Ekici. No other authors contributed to this work. Acknowledgement I would like to express my sincere gratitude to all the students who participated in this research. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. References Aimeur E, Amri S, Brassard G (2023) Fake News, Disinformation and Misinformation in Social Media: A Review. Social Netw Anal Min 30:1–36. https://doi.org/10.1007/s13278-023-01028-5 Akkurt S (2019) Sosyal Medyada Gerçekleşen İhlaller Karşısında Kişilik Hakkının Korunması. Seçkin, Istanbul Baptista JP, Gradim A (2020) Understanding Fake News Consumption: A Review. Social Sci 185:1–22. https://doi.org/10.3390/socsci9100185 Ekici Ş (2024) Bilişim ve Teknoloji Hukuku. Seçkin, Istanbul Ellison NB, Boyd DM (2013) Sociality Through Social Network Sites. In: Dutton WH (ed) The Oxford Handbook of Internet Studies. Oxford University Press, London, pp 151–172 Kaplan AM, Haenlein M (2010) Users of the World, Unite! The Challenges and Opportunities of Social Media. Bus Horiz 53:59–68. https://doi.org/10.1016/j.bushor.2009.09.003 Puska AA, Baroni LA, Pereira R (2024) Decoding the Sociotechnical Dimensions of Digital Misinformation: A Comprehensive Literature Review In:arXiv. https://doi.org/10.48550/ARXIV.2406.11853 Accessed 15 Jan 2026 TUIK (2025) Hanehalkı Bilişim Teknolojileri (BT) Kullanım Araştırması (Household Information Technology (IT) Usage Survey) 2025. In: Turkish Statistical Institute https: //data.tuik.gov.tr/Bulten/Index?p=Hanehalki-Bilisim-Teknolojileri-(BT)-Kullanim-Arastirmasi-2025-53925 Accessed 16 Jan 2026 Vosoughi S, Roy D, Aral S (2018) The Spread of True and False News Online. Science 6380:1146–1151. https://doi.org/10.1126/science.aap9559 We are Social (2025) Digital 2026 Global Overview Report. https://wearesocial.com/uk/blog/2025/10/digital-2026-global-overview-report/ Accessed 17 Jan 2026 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Submission checks completed at journal 20 Apr, 2026 First submitted to journal 14 Apr, 2026 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-9412586","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633495852,"identity":"415cfc12-996b-477b-a892-ec3aeb599a70","order_by":0,"name":"Şerafettin Ekici","email":"data:image/png;base64,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","orcid":"","institution":"Istanbul Medeniyet University","correspondingAuthor":true,"prefix":"","firstName":"Şerafettin","middleName":"","lastName":"Ekici","suffix":""}],"badges":[],"createdAt":"2026-04-14 08:25:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9412586/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9412586/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108429192,"identity":"08a2b391-004d-45ed-9b99-24d3bbbb4fac","added_by":"auto","created_at":"2026-05-04 14:18:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42790,"visible":true,"origin":"","legend":"\u003cp\u003eScatter Plot of Social Media Usage vs. Exposure to Disinformation\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9412586/v1/2277f8233734a69fc7a8e063.png"},{"id":108803933,"identity":"a3b43ca0-2f82-4c4e-8660-3a173337814e","added_by":"auto","created_at":"2026-05-08 15:11:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39582,"visible":true,"origin":"","legend":"\u003cp\u003eScatter Plot of Trend Content Checking vs. Exposure to Disinformation\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9412586/v1/9cdd1fd5ff6f4ed8dc2d1907.png"},{"id":108492681,"identity":"5c3bc325-334d-4485-93de-140904fb545d","added_by":"auto","created_at":"2026-05-05 09:58:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42669,"visible":true,"origin":"","legend":"\u003cp\u003eScatter Plot Matrix for Legal Inadequacy\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9412586/v1/1711387a2915ad2e0c97d9fb.png"},{"id":108493709,"identity":"f7859928-132c-4a4f-b307-13a3c2e89b3b","added_by":"auto","created_at":"2026-05-05 10:01:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38411,"visible":true,"origin":"","legend":"\u003cp\u003eScatter Plot Matrix for Education Inadequacy\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9412586/v1/0b9c9b36a905722ecd68dad6.png"},{"id":108429196,"identity":"5dad5b1c-5d7c-474d-a091-7f771d6141da","added_by":"auto","created_at":"2026-05-04 14:18:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":120718,"visible":true,"origin":"","legend":"\u003cp\u003eClustered Bar Chart - Faculty vs TPC 217/A Perception\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9412586/v1/1ac4c66614c858fc23a5ac32.png"},{"id":108813327,"identity":"9e30ab24-28f2-48eb-ae8a-f839a2faf9ae","added_by":"auto","created_at":"2026-05-08 16:15:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":689654,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9412586/v1/99db1bc8-e4eb-4efd-995c-152ed50d0ad4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAn Analysis of the Relationship Between Social Media Usage and Exposure to Disinformation Among University Students in Türki̇Ye\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRecent studies indicate that social media, which has become a part of our lives over approximately the past two decades, has been adopted at a considerably faster pace than traditional media tools in terms of information exchange and communication, and that the rate of social media usage is increasing rapidly each passing day (Baptista \u0026amp; Gradim, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to the \u0026ldquo;Digital 2026 Global Overview Report\u0026rdquo; (We are Social, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) published by We Are Social, the number of global social media user identities has reached 5.66\u0026nbsp;billion, corresponding to 68.7% of the world\u0026rsquo;s population. This total represents a 4.8% increase over the 12-month period leading up to October 2025, with the addition of 259\u0026nbsp;million new user identities.\u003c/p\u003e \u003cp\u003eSimilarly, the number of social media users in T\u0026uuml;rkiye is increasing rapidly. According to the report published by the Turkish Statistical Institute (TUIK), the rate of internet (and social media) use among individuals aged 16\u0026ndash;74 rose from 88.8% in 2024 to 90.9% in 2025 (TUIK, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe concept of social media has not been explicitly defined under Turkish law. However, numerous definitions of social media exist within the academic literature (Akkurt, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ekici, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ellison \u0026amp; Boyd, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kaplan \u0026amp; Haenlein, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Drawing upon the commonalities among these doctrinal definitions, social media can be described as environments and tools that enable users to exchange data, communicate, interact, form communities, and create content in internet-based settings\u0026mdash;either privately or publicly (Ekici, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDisinformation is defined as the intentional and systematic manipulation of information aimed at misleading individuals, institutions, or the state in a manner that adversely affects public order, security, or public health (Puska et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough social media has enhanced the ease of disseminating real-time information, its popularity has accelerated the speed and scope of intensive usage, thereby facilitating the spread of fake news (Aimeur et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, social media has emerged as a powerful source of disinformation in the contemporary era (Aimeur et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to a study conducted in 2018, false news across all categories of information was found to spread significantly faster, deeper, and more broadly than true news. Moreover, the impact of false content was particularly more pronounced in political news compared to topics such as terrorism, natural disasters, science, urban legends, or financial matters (Vosoughi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, as the production of manipulated and false content becomes easier, its detection more difficult, and as disinformation actors continuously adapt their tactics, fake news is likely to exert a considerable impact on society (Aimeur et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Rationale And Purpose Of The Study","content":"\u003cp\u003eExamining the social media usage habits of university students in T\u0026uuml;rkiye, their levels of exposure to disinformation, their awareness of such exposure, and their responses to disinformation is of critical importance for the development of policies pertaining to social media literacy and conscious usage in T\u0026uuml;rkiye. Furthermore, determining whether Article 217/A, added to the Turkish Penal Code (TPC) in 2022, is deemed sufficient will provide guidance for prospective legal regulations.\u003c/p\u003e \u003cp\u003eIn particular, when conducting studies on educational initiatives and legislative measures, it is necessary to analyze whether significant differences exist with respect to variables such as public/foundation university status, gender, and grade level.\u003c/p\u003e \u003cp\u003eUniversity students constitute a demographic of adult, young, and educated individuals who, as active users of social media, are particularly susceptible to disinformation. This study aims to systematically examine the relationships between university students\u0026rsquo; social media usage habits and their levels of exposure to disinformation.\u003c/p\u003e \u003cp\u003eThe primary objective of the research is to quantitatively test these relationships through five fundamental hypotheses and to develop policy recommendations in light of the findings obtained.\u003c/p\u003e"},{"header":"3. Method","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Research Design\u003c/h2\u003e \u003cp\u003eThis study is a cross-sectional study designed using the survey model, one of the quantitative research methods. The relational survey model was employed in the research. The questionnaire, created via Google Forms, was administered online to university students enrolled in public and foundation universities across T\u0026uuml;rkiye.\u003c/p\u003e \u003cp\u003eParticipants were selected on a voluntary basis. A total of 474 students from various departments of universities participated in the survey.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Population and Sample\u003c/h2\u003e \u003cp\u003eThe population of the study consists of university students in T\u0026uuml;rkiye. The sample comprises 474 university students selected through convenience sampling method. The demographic characteristics of the sample are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Charasteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerefer not to say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaculty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaw/Justice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolitical Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngineerin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTheology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Data Collection Instrument\u003c/h2\u003e \u003cp\u003eThe data collection instrument employed in this study was the \u0026ldquo;Social Media and Disinformation Questionnaire,\u0026rdquo; developed by the researchers (the questionnaire is presented as Appendix 1). The questionnaire consists of four sections:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDemographic information (5 items)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSocial media usage habits (6 items)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDisinformation experiences (5 items)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOpinions on disinformation countermeasures policies (5 items)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eValidity and reliability analyses of the questionnaire were conducted, and the Cronbach\u0026rsquo;s Alpha reliability coefficient was calculated as 0.84.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Findings","content":"\u003cp\u003e \u003cb\u003e4.1. General Statistics of Participants\u0026rsquo; Responses\u003c/b\u003e \u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe most frequently used platforms among participants were WhatsApp (95.6%), Instagram (91.1%), YouTube (89.0%), X (51.9%), and TikTok (41.1%) (respondents were allowed to select multiple options for this question). All participants were found to use more than one platform.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRegarding daily social media usage duration, 2.1% of participants reported using social media for less than 1 hour, 13.7% for 1\u0026ndash;2 hours, 34.8% for 2\u0026ndash;3 hours, 32.1% for 4\u0026ndash;5 hours, and 17.3% for more than 5 hours. Accordingly, 84.2% of all participants were found to use social media for more than 2 hours per day.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eConcerning the purposes of social media use (multiple responses allowed), 67.5% of participants reported following fashion/entertainment, 61.2% reading news, 57.6% following friends, 55.1% accessing educational/useful information, and 17.9% self-expression. These responses indicate that participants use social media for multiple purposes.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRegarding the frequency of viewing trending content, 8.6% of participants stated that they always view trending content, 38.8% often view it, and 34.4% sometimes view it. In contrast, 13.5% reported viewing it very rarely, and 4.6% stated they never view trending content. Thus, a total of 95.4% of participants were found to engage with trending content.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAs a source of news, 59.9% of participants preferred social media, 9.1% preferred television and radio channels, and only 0.8% preferred printed newspapers and magazines. These findings reveal that social media constitutes the primary source of news, while traditional media (printed press) lags considerably behind.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eConcerning exposure to disinformation, 3.4% of participants reported being constantly exposed, 9.7% frequently exposed, 48.3% exposed several times, and 6.5% exposed once. Meanwhile, 32.1% stated that they had never been exposed to disinformation. In total, 67.9% of participants had experienced disinformation at least once, with more than half having encountered it multiple times. Among those exposed to disinformation, 48.3% reported that their trust in the account sharing the disinformative content had completely ended, while 44.3% indicated that their trust had partially decreased. Conversely, 7.4% stated that their level of trust remained unchanged.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWith regard to the perceived harmfulness of disinformation, 40.5% of participants considered it completely harmful, and 33.1% considered it mostly harmful. In contrast, only 2.8% of participants regarded disinformation as mostly harmless or completely harmless. Other participants expressed that it was sometimes harmful and sometimes harmless. Thus, 73.6% of participants perceive disinformation as either completely or mostly harmful.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRegarding the adequacy of efforts to combat disinformation in T\u0026uuml;rkiye, 25.7% of participants stated that such efforts were completely insufficient, while 45.8% found them mostly insufficient. Accordingly, 71.5% of participants expressed that the fight against disinformation in T\u0026uuml;rkiye was entirely or largely inadequate. In comparison, only 4.9% of participants considered these efforts completely or mostly sufficient.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eConcerning the sufficiency of Article 217/A added to the Turkish Penal Code (TPC), 5.9% of participants found it completely sufficient, and 18.4% found it largely sufficient. Conversely, 19.6% of participants regarded the article as largely insufficient, and 8.0% as completely insufficient. Additionally, 48.1% of participants considered it moderately sufficient.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhen asked whether educational initiatives concerning disinformation in T\u0026uuml;rkiye were sufficient, 59.7% of participants responded that they were completely or largely insufficient. In contrast, only 5.7% of participants considered these educational efforts completely or largely sufficient, while 34.6% regarded them as moderately sufficient.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRegarding public service announcements (priority broadcasts) on combating disinformation in T\u0026uuml;rkiye, 57.7% of participants stated that they were completely or largely insufficient. Conversely, 5.7% of participants found them completely or largely sufficient, and 38.6% regarded them as moderately sufficient.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIn response to the question on which measures should be prioritized in combating disinformation (multiple responses allowed), 59.3% of participants emphasized legal regulations, 55.7% highlighted education in schools, 50.6% stressed awareness-raising on social media, and 38.0% underscored public service announcements (priority broadcasts).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Hypotheses\u003c/h2\u003e \u003cp\u003eFive hypotheses were proposed within the scope of the study:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eAs the duration of social media use increases, the frequency of exposure to disinformation also increases.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eAs the frequency of viewing trending content increases, the rate of exposure to disinformation also increases.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eAs the rate of exposure to disinformation increases, the perception that educational and legal measures concerning disinformation are insufficient also increases.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003eThere is a significant relationship between students\u0026rsquo; departments and their opinions regarding the sufficiency of Article 217/A of the Turkish Penal Code.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5\u003c/strong\u003e \u003cp\u003eThere is a significant relationship between students\u0026rsquo; gender and their level of exposure to disinformation.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Data Analysis Method\u003c/h2\u003e \u003cp\u003eIBM SPSS 25.0 software was employed for data analysis. The analyses were conducted as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDescriptive statistics (frequency, percentage, mean, standard deviation)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor hypothesis testing:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH1, H2, H3: Spearman correlation analysis\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH4, H5: Chi-square test of independence\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSignificance level: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEffect size interpretation: Cohen\u0026rsquo;s (1988) criteria were utilized.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Hypothesis Analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003e4.4.1. Analysis of Hypothesis H1: \u0026ldquo;As the duration of social media use increases, the frequency of exposure to disinformation also increases\u0026rdquo;.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe descriptive statistics regarding participants\u0026rsquo; responses to the question on daily social media usage duration and their responses to the question on exposure to disinformation are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Media Use (hours/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure to Disinformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution graph of participants\u0026rsquo; responses regarding daily social media usage duration and their responses regarding exposure to disinformation is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor Hypothesis H1, \u0026ldquo;duration of social media use\u0026rdquo; (ordinal) constitutes the independent variable, while \u0026ldquo;frequency of exposure\u0026rdquo; (ordinal) constitutes the dependent variable. Both variables are ordinal categorical data. In order to measure the direction and strength of the linear relationship between these ordinal variables, the Spearman correlation test was applied to the independent and dependent variables. The Spearman correlation results for the variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman Correlation - Social Media Usage vs Disinformation Exposure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect Size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Media Use vs Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.324, 0.492)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium-Large\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMean scores of exposure to disinformation by social media usage duration are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDisinformation Exposure by Social Media Usage Frequency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsage Frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Exposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow Exposure (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh Exposure (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor Hypothesis H1, the Spearman correlation coefficient was found to be ρ\u0026thinsp;=\u0026thinsp;0.412, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. This result indicates a statistically significant, moderately strong positive correlation (correlation coefficient\u0026thinsp;=\u0026thinsp;0.412) between the frequency of social media use and exposure to disinformation. The positive correlation (ρ = +0.412) demonstrates that as the frequency of social media use increases, the frequency of exposure to disinformation also increases. Among students using social media for 4\u0026ndash;5 hours per day, the high exposure rate was 41.1%, whereas this rate increased to 56.5% among those using social media for more than 5 hours daily. The 95% confidence interval (0.324, 0.492) confirms that the relationship is statistically reliable and positive in direction.\u003c/p\u003e \u003cp\u003eIn conclusion, Hypothesis H1 (\u0026ldquo;As the duration of social media use increases, the frequency of exposure to disinformation also increases\u0026rdquo;) is supported by the results of the Spearman correlation analysis. A statistically significant relationship exists between the duration of social media use and exposure to disinformation. Based on the research findings, Hypothesis H1 is confirmed.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.4.2. Analysis of Hypothesis H2: \u0026ldquo;As the frequency of viewing trending content increases, the rate of exposure to disinformation also increases\u0026rdquo;.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe descriptive statistics regarding participants\u0026rsquo; responses on the frequency of following trending content and their responses on the frequency of exposure to disinformation are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChecking Trend Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure to Disinformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eTrend Tracking\u003c/b\u003e:\u0026nbsp;1\u0026thinsp;=\u0026thinsp;Never, 2\u0026thinsp;=\u0026thinsp;Very Rarely, 3\u0026thinsp;=\u0026thinsp;Sometimes, 4\u0026thinsp;=\u0026thinsp;Often, 5\u0026thinsp;=\u0026thinsp;Always\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution graph of participants\u0026rsquo; responses regarding the frequency of viewing trending content and their responses regarding exposure to disinformation is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor this hypothesis, frequency of following trending content was taken as the independent variable (ordinal), and the rate of exposure to disinformation was taken as the dependent variable (ordinal). Both variables are ordinal categorical data. In order to measure the direction and strength of the linear relationship between these ordinal variables, the Spearman correlation test was applied to the independent and dependent variables.\u003c/p\u003e \u003cp\u003eThe Spearman correlation results between the variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman Correlation - Trend Checking vs Disinformation Exposure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect Size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrend Checking vs Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.269, 0.430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMean scores of exposure to disinformation by frequency of trend checking content are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDisinformation Exposure by Trend Checking Frequency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrend Checking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Exposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow Exposure (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh Exposure (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor Hypothesis H2, the Spearman correlation coefficient was found to be ρ\u0026thinsp;=\u0026thinsp;0.352, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. This result indicates a statistically significant, moderate positive correlation between the frequency of viewing trending content and exposure to disinformation. Among students who always follow trending content, 52.8% are exposed to high levels of disinformation. The 95% confidence interval (0.269, 0.430) confirms that the relationship is statistically reliable and positive in direction.\u003c/p\u003e \u003cp\u003eIn conclusion, Hypothesis H2 (\u0026ldquo;As the frequency of viewing trending content increases, the rate of exposure to disinformation also increases\u0026rdquo;) is supported by the results of the Spearman correlation analysis. A statistically significant relationship exists between the frequency of viewing trending content and exposure to disinformation. Based on the research findings, Hypothesis H2 is confirmed.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.4.3. Analysis of Hypothesis H3: \u0026ldquo;As the rate of exposure to disinformation increases, the rate of perceiving educational and legal measures concerning disinformation as insufficient also increases\u0026rdquo;\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe descriptive statistics regarding participants\u0026rsquo; responses on the rate of exposure to disinformation and their responses on the insufficiency of education and legal regulations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure to Disinformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Inadequacy of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Inadequacy of Legal Regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution graph of participants\u0026rsquo; responses regarding exposure to disinformation and their responses regarding the inadequacy of education is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, while the distribution graph regarding the inadequacy of legal regulations is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor this hypothesis, \u0026ldquo;frequency of exposure to disinformation\u0026rdquo; was taken as the independent variable (ordinal), and \u0026ldquo;perception of adequacy\u0026rdquo; was taken as the dependent variable (ordinal). Both variables are ordinal categorical data. In order to measure the direction and strength of the linear relationship between these ordinal variables, the Spearman correlation test was applied to the independent and dependent variables.\u003c/p\u003e \u003cp\u003eThe Spearman correlation results between the variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman Correlations - Exposure vs Perceived Inadequacy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect Size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure vs Education Inadequacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.342, 0.489)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium-Large\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure vs Legal Regulation Inadequacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.131, 0.302)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSmall-Medium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation vs Legal Inadequacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(0.378, 0.520)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedium-Large\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe detailed proportional outputs regarding exposure to disinformation and the perception of inadequacy of education and legal regulations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe perception of inadequacy of education and legal regulations in relation to exposure to disinformation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Education Inadequacy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Legal Inadequacy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDiff (Legal-Edu)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA Few Times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequently\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstantly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor Hypothesis H3, the Spearman correlation coefficient for the \u0026ldquo;perception of educational inadequacy\u0026rdquo; was found to be ρ\u0026thinsp;=\u0026thinsp;0.418, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Accordingly, there is a moderately strong positive correlation between participants\u0026rsquo; exposure to disinformation and their perception of educational inadequacy. Based on this finding, students who are more frequently exposed to disinformation perceive educational efforts as more insufficient.\u003c/p\u003e \u003cp\u003eFor Hypothesis H3, the Spearman correlation coefficient for the \u0026ldquo;perception of legal regulation inadequacy\u0026rdquo; was found to be ρ\u0026thinsp;=\u0026thinsp;0.218, p\u0026thinsp;=\u0026thinsp;0.001. Accordingly, there is a moderate positive correlation between participants\u0026rsquo; exposure to disinformation and their perception of legal regulations as insufficient. Based on this finding, students who are more frequently exposed to disinformation perceive legal regulations as more insufficient.\u003c/p\u003e \u003cp\u003eIn conclusion regarding Hypothesis H3: As exposure to disinformation increases, students find existing educational and legal measures more insufficient. Overall, students perceive legal regulations (mean\u0026thinsp;=\u0026thinsp;3.80) as more insufficient than educational efforts (mean\u0026thinsp;=\u0026thinsp;3.65). The relationship between exposure to disinformation and perception of educational inadequacy (ρ\u0026thinsp;=\u0026thinsp;0.418) is approximately twice as strong as that for perception of legal inadequacy (ρ\u0026thinsp;=\u0026thinsp;0.218). Among those never exposed to disinformation, the perception of legal inadequacy is 0.65 points higher than that of educational inadequacy, whereas among those constantly exposed, this difference decreases to -0.10. Cumulatively, as the level of exposure increases, the perception of inadequacy in both dimensions increases; however, the increase in the educational dimension is more pronounced.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.4.4. Analysis of Hypothesis H4: \u0026ldquo;There is a significant relationship between students\u0026rsquo; departments and their opinions regarding the sufficiency of Article 217/A of the Turkish Penal Code\u0026rdquo;\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor this hypothesis, \u0026ldquo;students\u0026rsquo; departments\u0026rdquo; was taken as the independent variable (nominal/categorical), while \u0026ldquo;opinion on the sufficiency of the regulation in Article 217/A of the Turkish Penal Code\u0026rdquo; was taken as the dependent variable (ordinal/categorical). For the analysis, the dependent variable categories were categorized as follows: Adequate: \u0026ldquo;Completely sufficient\u0026rdquo; + \u0026ldquo;Largely sufficient\u0026rdquo;; Neutral: \u0026ldquo;Moderately sufficient\u0026rdquo;; Inadequate: \u0026ldquo;Largely insufficient\u0026rdquo; + \u0026ldquo;Completely insufficient.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThe observed frequencies and percentage distribution by faculty of participants\u0026rsquo; responses to the question regarding the sufficiency of Article 217/A of the Turkish Penal Code are presented in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eObserved Frequencies - Faculty vs TPC 217/A Perception\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaculty / TPC Perception\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInadequate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRow Total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaw/Justice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (%17,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (%14,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (%68,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185 (%100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (1%8,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (%12,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (%68,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95 (%100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngineering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (%27,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (%17,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (%55,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (%100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (%31,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (%20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (%48,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (%100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (%28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (%16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (%56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (%100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (%24,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (%13,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (%61,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89 (%100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColumn Total / Overall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (%21,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (%14,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e302 (%63,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e474 (%100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe clustered bar chart (Faculty vs. TPC 217/A Perception) based on participants\u0026rsquo; responses is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo analyze whether there is a relationship between the two categorical variables (department and perception), the \u0026ldquo;Chi-Square Test of Independence\u0026rdquo; was conducted. The test statistics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChi-Square Test Results - Faculty vs TPC 217/A Perception\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect Size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePearson Chi-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCramer\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikelihood Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinear-by-Linear Association\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN of Valid Cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the test conducted, for Hypothesis H4, the Pearson Chi-Square value was found to be χ\u0026sup2;(10)\u0026thinsp;=\u0026thinsp;18.456, p\u0026thinsp;=\u0026thinsp;0.047. Based on these results, since p\u0026thinsp;=\u0026thinsp;0.047\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Hypothesis H4 (\u0026ldquo;There is a significant relationship between students\u0026rsquo; departments and their opinions regarding the sufficiency of Article 217/A of the Turkish Penal Code\u0026rdquo;) is statistically supported. However, as Cramer\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;0.139, it can be stated that the relationship is weak.\u003c/p\u003e \u003cp\u003eAccording to the responses, the majority of students across all departments (63.7%) find Article 217/A of the TPC insufficient. In other words, a general perception of inadequacy prevails across all departments. Law and Political Science students stand out as the group with the highest perception of inadequacy (68.1% \u0026minus;\u0026thinsp;68.4%). The perception of inadequacy is slightly lower among students in Engineering, Theology, and Education faculties (48\u0026ndash;56%). Accordingly, it can be stated that Law and Political Science students are the most critical groups (with the highest rate of inadequacy perception). Theology students have the highest perception of \u0026ldquo;sufficiency\u0026rdquo; (31.4%). Engineering and Education faculty students exhibit a moderate level of inadequacy perception.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.4.5 Analysis of Hypothesis H5: \u0026ldquo;There is a significant relationship between students\u0026rsquo; gender and their level of exposure to disinformation\u0026rdquo;\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor this hypothesis, students\u0026rsquo; gender was taken as the independent variable (nominal), while the status of understanding disinformation was taken as the dependent variable (nominal/dichotomous). During the analysis, responses were coded as follows: those who selected the option \u0026ldquo;I do not understand disinformation\u0026rdquo; (0) and those who indicated \u0026ldquo;I understand it using some method\u0026rdquo; (1).\u003c/p\u003e \u003cp\u003eThe observed frequencies of the responses provided by participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eObserved Frequencies - Gender vs Disinformation Exposure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender / Exposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Exposed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Exposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Exposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRow Total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (%16,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188 (%63,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (%19,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e295 (%100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (%12,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (%62,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (%25,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175 (%100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColumn Total / Overall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (%15,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297 (%63,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (%21,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e470 (%100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo analyze the relationship between the two nominal variables (gender and exposure to disinformation), the \u0026ldquo;Chi-Square Test of Independence\u0026rdquo; was conducted. The test statistics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChi-Square Test Results - Gender vs Disinformation Exposure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect Size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePearson Chi-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCramer\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikelihood Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinear-by-Linear Association\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFisher\u0026rsquo;s Exact Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN of Valid Cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs a result of the test conducted, χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;6.892, p\u0026thinsp;=\u0026thinsp;0.032 was found. Thus, a statistically significant, albeit weak, difference close to the critical threshold was found between the rates at which male and female students can detect disinformation (i.e., those who responded \u0026ldquo;I understand it\u0026rdquo;).\u003c/p\u003e \u003cp\u003eThe risk analysis for high exposure by gender is presented in Table\u0026nbsp;\u003cspan refid=\"Tab15\" class=\"InternalRef\"\u003e15\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk Analysis for High Exposure by Gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Ratio (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.02, 1.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOdds Ratio (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.03, 1.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute Risk Difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;5.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(+\u0026thinsp;0.5%, +\u0026thinsp;11.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber Needed to Harm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn conclusion regarding Hypothesis H5, there is a statistically significant relationship between students\u0026rsquo; gender and their levels of exposure to disinformation. While 25.1% of male students are exposed to high levels of disinformation, this rate is 19.3% among female students. Male students have a 30% higher risk of high-level exposure compared to female students (Risk Ratio: 1.30). The rate of never being exposed to disinformation is higher among female students (16.9%) than among male students (12.6%).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. General Evaluation And Conclusion","content":"\u003cp\u003eAll five hypotheses established in this study were statistically accepted. A summary of all hypothesis test results is presented in Table\u0026nbsp;\u003cspan refid=\"Tab16\" class=\"InternalRef\"\u003e16\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab16\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 16\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of all hypothesis test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupport Status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003eρ\u0026thinsp;=\u0026thinsp;0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedium-Large\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003eρ\u0026thinsp;=\u0026thinsp;0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH3a\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003eρ\u0026thinsp;=\u0026thinsp;0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedium-Large\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH3b\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003eρ\u0026thinsp;=\u0026thinsp;0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall-Medium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-Square Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2; = 18.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-Square Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2; = 6.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs a result of the research conducted and the tests applied to the hypotheses, the main findings are as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eResearch findings demonstrated a strong positive correlation between the duration of social media use and exposure to disinformation (ρ\u0026thinsp;=\u0026thinsp;0.412). As time spent on social media increases, algorithms directing users to similar content and creating echo chambers elevate the risk of exposure to disinformation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe moderate correlation between the frequency of viewing trending content and exposure to disinformation (ρ\u0026thinsp;=\u0026thinsp;0.352) indicates that the trending mechanisms of social media platforms play a significant role in the spread of disinformation. This finding suggests that the content prioritization systems of algorithms need to be reviewed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe fact that students exposed to disinformation find educational and legal measures more insufficient indicates that experience shapes perceptions. The strong correlation, particularly in the perception of educational inadequacy (ρ\u0026thinsp;=\u0026thinsp;0.418), reflects a widespread belief that current media literacy education is insufficient.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe significant relationship between faculty type and opinions on the sufficiency of Article 217/A of the TPC shows that students\u0026rsquo; academic disciplines influence their views on legal regulations. The more critical stance of Law and Political Science students may stem from their accumulated knowledge in these fields.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMale students are exposed to disinformation more frequently. This can be explained by gender-differentiated social media usage habits and content preferences. This finding suggests the need to develop gender-specific digital literacy programs.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn light of these fundamental findings, our recommendations for safer use of social media are as follows:\u003c/p\u003e \u003cp\u003e1. Limiting the duration of social media use may reduce exposure to disinformation. Efforts should be made to decrease students\u0026rsquo; social media usage time. Additionally, content moderation mechanisms should be developed for social media platforms.\u003c/p\u003e \u003cp\u003e2. Regulating trending mechanisms could prevent the spread of false information. In this context, measures should be implemented to prevent the creation of trending content, particularly by bot accounts.\u003c/p\u003e \u003cp\u003e3. Faculty-based educational programs on social media literacy and awareness against disinformation should be developed, and such training should be made compulsory.\u003c/p\u003e \u003cp\u003e4. Gender-specific media literacy programs should be established.\u003c/p\u003e \u003cp\u003e5. Article 217/A of the Turkish Penal Code (TPC) should be reviewed and revised, and a more effective legal regulation should be enacted.\u003c/p\u003e \u003cp\u003e \u003cb\u003e6. Limitations of The Study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study has a cross-sectional design; therefore, causal relationships cannot be established.\u003c/p\u003e \u003cp\u003eThe data are based on self-report, posing a risk of social desirability bias.\u003c/p\u003e \u003cp\u003eThe sample is limited to Turkish university students.\u003c/p\u003e \u003cp\u003eThe proportion of female participants is higher than that of male participants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical approval for conducting this research was granted by the Social and Human Sciences Ethics Committee of Istanbul Medeniyet University, with the decision dated December 5, 2025, and numbered 2025/10.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e Throughout the research process, ethical rules were adhered to, and participants were informed at the beginning of the questionnaire about the purpose of the survey, its ethical framework, and the manner in which the data would be processed.\u003c/p\u003e \u003cp\u003eParticipants in the survey are not linked to the research results in any way. All data were collected anonymously. The data have been stored in accordance with the principle of confidentiality.\u003c/p\u003e \u003cp\u003eTherefore, no ethical issues are present.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe research underlying this article and the entire manuscript were prepared solely by Assoc. Prof. Dr. Şerafettin Ekici. No other authors contributed to this work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI would like to express my sincere gratitude to all the students who participated in this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAimeur E, Amri S, Brassard G (2023) Fake News, Disinformation and Misinformation in Social Media: A Review. 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Science 6380:1146\u0026ndash;1151. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aap9559\u003c/span\u003e\u003cspan address=\"10.1126/science.aap9559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWe are Social (2025) Digital 2026 Global Overview Report. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wearesocial.com/uk/blog/2025/10/digital-2026-global-overview-report/\u003c/span\u003e\u003cspan address=\"https://wearesocial.com/uk/blog/2025/10/digital-2026-global-overview-report/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 17 Jan 2026\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"sn-social-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"SN Social Sciences","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Disinformation, social media, university students, media literacy, Turkish Penal Code Article 217/A","lastPublishedDoi":"10.21203/rs.3.rs-9412586/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9412586/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to examine the relationship between university students' social media usage habits and their levels of exposure to disinformation. The research was conducted with 474 university students. Spearman correlation analysis and the Chi-square test of independence were used to test the hypotheses. Findings revealed that increased duration of social media use (ρ\u0026thinsp;=\u0026thinsp;0.412, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and frequency of viewing trending content (ρ\u0026thinsp;=\u0026thinsp;0.352, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with higher rates of exposure to disinformation. Furthermore, students exposed to disinformation were found to perceive both educational (ρ\u0026thinsp;=\u0026thinsp;0.418, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and legal regulatory measures (ρ\u0026thinsp;=\u0026thinsp;0.218, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as insufficient. Significant relationships were identified between faculty type and opinions regarding the adequacy of Article 217/A of the Turkish Penal Code (χ\u0026sup2;=18.456, p\u0026thinsp;=\u0026thinsp;0.047), as well as between gender and level of exposure to disinformation (χ\u0026sup2;=6.892, p\u0026thinsp;=\u0026thinsp;0.032). The results indicate the necessity of revising digital literacy education and legal regulations. In this context, this article recommends limiting social media usage, undertaking efforts to prevent the creation of trending content particularly by bot accounts, developing faculty-based mandatory educational programs on social media literacy and awareness against disinformation, establishing gender-specific social media literacy programs, and revising Article 217/A of the Turkish Penal Code.\u003c/p\u003e","manuscriptTitle":"An Analysis of the Relationship Between Social Media Usage and Exposure to Disinformation Among University Students in Türki̇Ye","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 14:18:04","doi":"10.21203/rs.3.rs-9412586/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"147334830935723547253259591656315436118","date":"2026-04-29T13:21:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T04:24:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275807007112051695713055563392621267240","date":"2026-04-25T13:34:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T02:41:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T11:11:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-20T11:11:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"SN Social Sciences","date":"2026-04-14T08:18:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"sn-social-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"SN Social Sciences","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4ff6351c-6ed4-4993-a235-992dcef0a5d5","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T14:18:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 14:18:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9412586","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9412586","identity":"rs-9412586","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}