E-Arabia Felix: Exploring Positive Social Media Use and Its Association with Wellbeing in the Gulf Arab States

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Most studies exploring the wellbeing implications of social media have focused on deficit states and problematic use, frequently examining posited links with psychopathology, loneliness, and broader health concerns. Fewer studies attempt to explore how social media use might also contribute to subjective wellbeing. Grounded in positive psychology, this study examined the relationship between positive social media use (PSMU) and wellbeing. Derived from a large-scale cross-national survey, focusing on three Gulf Arab States (UAE, Saudi Arabia, and Kuwait), participants ( N = 3,002) completed a novel five-item measure of positive social media use, assessing the extent to which social media was perceived as contributing to positive emotions, engagement, relationships, meaning and accomplishment (PERMA). Participants also completed single-item measures of active social media use, life satisfaction, autonomy, competence, and relatedness. PSMU was correlated with life satisfaction, autonomy, competence, and relatedness. A regression analysis revealed that the association between positive social media use and wellbeing variables persisted after controlling for active use and demographic covariates. The results are discussed in the context of exploring how social media use might make positive contributions to wellbeing. Psychology PERMA Social Media Arab Internet Wellbeing Figures Figure 1 Introduction The Hyperconnected The Gulf Arab States, comprising Bahrain, Kuwait, Oman, Qatar, the United Arab Emirates (UAE), and the Kingdom of Saudi Arabia (KSA), have all undergone rapid social, technological, and economic changes throughout the 20th and 21st centuries. Although each state is unique, they also share many commonalities. Along with borders and language (Arabic), they share history, heritage, culture, and religion to varying degrees (Thomas, 2014 ). With similar systems of political governance, the Gulf Arab States have all pursued aggressive programs of social, economic, and technological development (al-Khazi, 2008 ). At the time of writing, all six states have 100% population coverage by mobile internet technology (3G or higher), according to 2023 data reported by the International Telecommunications Union ( 2025 ). Similarly, in a survey of 53 nations, Saudi Arabia had the highest rate of social media penetration (the number of active social media users as a percentage of the total population), while the UAE was ranked second (Statista 2025). With widespread high-speed internet access and high levels of social media adoption, the Gulf region is well-positioned to benefit from digital technologies, while also being at the forefront of navigating the challenges of the current and emerging online world. Digital Illbeing The notion that digital technology use could become problematic in a psychopathological sense first attracted scientific research attention in the mid-1990s. Drawing parallels with pathological gambling, Griffiths ( 1995 ) introduced the concept of technological addictions, conceived of as behavioural addictions involving human-computer interactions. It took over two decades for this concept to receive international nosological endorsement; however, in 2018-19, the World Health Organisation (2018) included gaming disorder within the behavioural addictions section of the International Classification of Diseases, 11th Revision (ICD-11). The ICD-11 also enables the diagnosis of other internet-related problems within the section’s residual categories, for example, “disorders due to addictive behaviours, unspecified” (Brand et al., 2020 ; Lindenberg et al., 2022 ). It is here that putative technology-related behavioural addictions, such as problematic social media use, could also be diagnosed. Beyond technological addictions, there is also the contested idea that social media, in particular, is implicated in rising rates of adolescent mental health problems in some nations (Baird et al., 2025 ). For example, Twenge et al. ( 2018 ) linked the rising rates of US adolescent depression and suicide between 2010 and 2015 with the increased use of social media during the same period. Based on survey data for a nationally representative sample of over half a million US adolescents (ages 13 to 18), Twenge et al. found that time spent on social media was positively correlated with mental health issues such as depression. Conversely, time spent on non-screen activities, such as in-person social interaction, sports, and homework, was negatively correlated with mental health issues (Twenge et al., 2018 ). Other studies go beyond the rather blunt screen time or social media usage metrics and look more closely at what people are actually doing when they are engaged with these platforms. For example, several studies have differentiated between active (interacting, commenting, posting) and passive social media use, for instance, aimless scrolling through a timeline or repetitively switching between apps (Valkenburg et al., 2022 ). Unsurprisingly, there is some evidence that passive use is associated with psychopathology and negative states, while active use is associated with increased wellbeing. For example, several studies have concluded that passive social media use is often preceded by feelings of loneliness (O’Day & Heimberg, 2021 ). Similarly, in an experimental lab-based survey, instructing people to use social media passively (rather than actively) led to decreases in subjective wellbeing over time (Verduyn et al., 2015 ). In a review of the active-passive social media use literature, Valkenburg et al ( 2022 ) conclude that even the active-passive distinction is too coarse to reliably identify meaningful associations with well-/ill-being, suggesting that future studies need to also consider the content and pre-existing affective state of the user. With a few notable exceptions, most of this social media–pathology research is correlational, telling us little or nothing about causality and possible mechanisms of action. Furthermore, not all of these correlational studies find the posited association between frequency of social media use (screen time), style of social media use (active passive) and psychopathology/illbeing. For example, in a study spanning six years, Heffer et al. ( 2019 ) report that the frequency of social-media use (hours-per-day) did not predict depressive symptoms among a large US college sample. Similarly, in a cross-sectional study of adolescents, spanning 29 European countries, Boer et al ( 2020 ) found that frequency/intensity of social media use was uncorrelated with psychological wellbeing. In fact, in some nations, those countries where intense social media use was highly prevalent (normalised), the frequency of use was linked to better psychological wellbeing. Similar positive wellbeing associations have also been reported for time spent playing specific online video games (Johannes et al., 2021 ). The fact that the links between screentime (duration and frequency of social media use) and psychological wellbeing remain equivocal is not to minimise some of the clear harms that are present on these platforms. For example, pro-ana (anorexia nervosa) accounts and social media content romanticising and promoting suicide and self-harm are unequivocally problematic (Baird et al., 2025 ). The bulk of psychological research to date exploring the use of digital technologies has focused on deficit states and the potential detrimental health impacts of digital technologies, particularly gaming and social media use. There is, however, also the possibility that social media and gaming can and do contribute to an individual’s wellbeing. Digital Wellbeing Despite the World Health Organisation’s (1948) constitutional affirmation that "Health is … not merely the absence of disease or infirmity.", attempts to measure health and wellbeing are still frequently fixated on assessing the absence of symptoms or deficit states (ill-being). For example, we have numerous measures of smartphone/internet use (Chen et al., 2020 ; Ghali et al., 2019 ; King et al., 2020 ), social media use (van den Eijnden et al., 2016 ) and gaming behaviours (Pontes & Griffiths, 2015 ) that focus on pathology. Much of the work undertaken in this area to date has essentially catalogued and explored how personal digital technologies potentially contribute to ill health, psychological disorders, unpleasant, and maladaptive states (e.g., loneliness, social anxiety, hyper-distractibility). Conversely, there are very few, if any, widely used measures assessing positive constructs such as healthy gaming, positive social media use, or satisfaction with online life. We might erroneously conclude that a person has a healthy, wellbeing-promoting, relationship with technology based on their attaining low scores on measures of online pathology. The absence of pathology is not evidence of flourishing. A review, titled Online Wellbeing , drew a similar conclusion (Ong et al., 2021 ). This study identified 56 subjective wellbeing scales; however, none of them were specifically designed to measure online wellbeing or explore how the online world might contribute to an individual’s wellbeing. This relative gap in the literature suggests the need to explore the presence of digital/online wellbeing through a positive psychology lens. This study examines the concept of positive social media use through the lens of Seligman’s ( 2011 ) PERMA model. PERMA proposes five elements (building blocks) that are important for the attainment of subjective wellbeing. Specifically, the components are positive emotions, engagement, relationships, meaning, and achievement/accomplishment. Each of these elements is measurable, can be independent of the others, and is intrinsically motivating, that is, pursued for its own sake (Seligman, 2018 ). Ultimately, the PERMA model is pragmatic, offering a well-articulated framework of measurable components that can serve as targets for interventions aimed at promoting human flourishing. In the present study, we use the PERMA model to inform the development of a measure of positive social media use. This self-report scale explores the extent to which an individual’s social media use is perceived as making a positive contribution to each of the PERMA components. We focus on citizens and residents of the Gulf Arab States, as these nations all have similarly high rates of social media penetration and widespread access to high-speed mobile internet. In line with the PERMA framework, we hypothesise that positive social media use will be correlated with other measures associated with wellbeing and optimal human functioning, including life satisfaction (OECD, 2017 )and basic psychological needs, specifically autonomy, relatedness, and competence (Chen et al., 2015 ). These basic psychological needs are identified within Self-Determination Theory (Ryan & Deci, 2000 ) as essential for optimal wellbeing and performance. Finally, we also propose that positive social media use will be associated with active social media use, given the previously - albeit inconsistently - observed associations between active use and better wellbeing (Valkenburg et al., 2022 ). Method Study Design This study is based on a subset of data from our Global Digital Wellbeing Survey (DWS). This was an extensive cross-national study which covered 35 nations. This online study spanned seven world regions and was administered by PSB Insights, a multinational analytics consultancy with substantial experience in cross-national polling and cross-sectional survey research. In this analysis, we extract and focus on the three culturally similar and hyperconnected Gulf Arab States included in the DWS: Kuwait, the United Arab Emirates (UAE), and the Kingdom of Saudi Arabia (KSA). For these Gulf-based participants, survey materials were translated and back-translated from English into Arabic, and made available online in both languages. Participants Participants in the present study were recruited from pre-existing participant panels. The survey aimed to obtain nationally representative samples ( N = 1,000) of the internet-using adult population for each territory included in the DWS. Potential participants (registered panellists) received invitations via email, resulting in an overall response rate of 17.37%. Automated data quality checks ensured that non-completers were excluded, along with respondents who completed the survey with illogical and stereotyped response patterns. Similarly, participants who finished the study at an unfeasible speed were also excluded. The mean exclusion rate was 18.7%. Oversampling ensured each nation comprised approximately 1,000 consenting adult participants. All data were collected between June 12th and July 11th, 2023. The study was reviewed and approved by the King Abdulaziz Centre for World Culture (Ithra) Internal Review Board (IRB 202371). The mean age of participants from the Gulf Arab States was 33.85 ( SD = 8.37). Table 1 presents additional sample characteristics. Table 1 Sample Characteristics of Respondents from the Participating Gulf States Variable Group N (%) Female Yes 1,059 (35.27) No 1,935 (64.72) Jobseeker Yes 121 (4.03) No 2,881 (95.96) College Graduate Yes 2,282 (76.01) No 720 (23.98) Parent Yes 1,335 (44.47) No 1,667 (55.52) Of these 3,002 participants, 88.87% (N = 2,668) responded affirmatively to a screening question about social media (Twitter, Instagram, TikTok, etc.) use over the past 12 months. Those who answered affirmatively then completed the social media usage measures included in the study. Measures The study’s posited predictor variable was positive social media use. Our hypotheses suggest this type of social media use will be positively correlated with wellbeing metrics. We also included a measure of active social media use as a suspected covariate of positive social media use and wellbeing. Both of these measures are described below. Positive Social Media Use (PSMU) Scale The PSMU scale is a five-item self-report measure designed to assess positive social media use, grounded in the PERMA model (Thomas & AlJedawi, 2025 ). PSMUS was designed to determine how frequently social media is perceived as contributing to wellbeing. The scale is derived from the PERMA-Profiler (Butler & Kern, 2016 ), which is grounded in Seligman’s ( 2011 ; 2018 ) five-dimensional PERMA model of human wellbeing: Positive Emotion (P), Engagement (E), Relationships (R), Meaning (M), and Accomplishment (A). The scale instructs respondents to consider their personal (i.e., non-work-related) use of social media over the past six months when responding to the following PERMA-aligned items: Using social media leaves me feeling positive/joyful/happy. (P) I become pleasantly absorbed in what I’m doing on social media. (E) I feel valued and supported by my social media friends/contacts. (R) My use of social media is well-aligned with my values. (M) Social media contributes towards my accomplishing goals that are important to me. (A) Each PSMU item is frequency-rated on a 4-point scale, from 1 ( Never ) to 4 ( Very often ). A total score is calculated by summing all five items, with higher scores indicating more positive perceived psychosocial outcomes associated with social media use. Active/Passive Social Media Use We also included a single-item measure of active versus passive social media use. This item asked: “Thinking about the past 12 months, how would you describe your use of social media? Please rate on a scale of 1 to 4, where four represents active social media use and one represents passive social media use.” We defined passive use as: “only reading comments, watching videos and looking at images without further interaction.” In contrast, active engagement was described as: “making comments, interacting with peers and posting or liking content regularly.” Participants indicated if they (1) only use social media passively, (2) use social media more passively than actively, (3) use social media more actively than passively, or (4) only use social media actively. The study's dependent variables were measures of life satisfaction and psychological well-being. These four single-item measures are detailed below. Basic Psychological Need Satisfaction and Frustration (single-item scales) The Basic Psychological Need Satisfaction and Frustration Scale is grounded in Self Determination Theory, which posits that three basic psychological needs (autonomy, relatedness, and competence) are essential for psychological well-being and optimal human functioning(Chen et al., 2015 ; Martela & Ryan, 2024 ). The original BPNSF scale has 12 items (Chen et al., 2015 ), four per each psychological need. To create single-item metrics, Martela and Ryan ( 2021 ) identified the best-performing item for each of the psychological needs: autonomy, relatedness, and competence. The performance of these single-item measures was compared to several other longer need satisfaction scales. In all cases, the single items loaded well on the corresponding longer scales and were near identical in their correlation with criterion variables, suggesting that they are valid and useful need satisfaction measures in research contexts requiring brevity (Martela & Ryan, 2024 ). The identified single-item measures and the items used in this study were as follows: I feel that my decisions reflect what I really want (Autonomy) I feel confident that I can do things well (Competence) I feel close and connected with other people who are important to me (Relatedness) Each item was scored on a 4-point Likert scale, ranging from "strongly disagree" (1) to "strongly agree" (4). Organisation for Economic Co-operation and Development (OECD) Life Satisfaction The OECD’s life satisfaction item purports to measure how people evaluate their life as a whole rather than their current feelings. The measure asks respondents to rate their general satisfaction with life on a scale of 0 to 10 (“Overall, how satisfied are you with your life?”). With greater life satisfaction indicated by higher values, the maximum being 10. The single-item measure was developed as part of the OECD’s Better Life Index and has been widely used in their international survey work (OECD, 2017 ). Atroszko et al ( 2017 ) Report a highly significant and positive correlation between the single-item life satisfaction measure and the five-item Satisfaction with Life Scale. Similarly, both measures correlated in the same ways with other well-being and personality indicators. Abdel-Khalek ( 2006 ) reports similarly favourable concurrent validity between multi-item and single-item measures of life satisfaction. Research questions and analytic plan This study seeks to answer four questions: (1) Is PSMU correlated with life satisfaction and existing subjective wellbeing metrics? (2) Is PSMU correlated with active social media use? (3) Does PSMU remain predictive of life satisfaction and wellbeing after controlling for covariates? (4) Do these nations exhibit significant variations in their levels of PSMU? To answer these primary questions, we use correlational and linear regression analyses. The linear regression models involved using each of the wellbeing variables (life satisfaction, autonomy, competence and relatedness) as dependent/criterion variables, while PSMU was the predictor variable. Active social media use and several demographic variables were entered as covariates. To explore between-nation differences, we employed a one-way ANOVA with a post hoc Tukey test. Results The data for age, active/passive social media use and positive social media use were normally distributed, while scores for life satisfaction, autonomy, competence, and relatedness were all negatively skewed. Table 1 provides the mean, standard deviation, and correlations for all the study’s continuous variables. Table 1 Correlations and Descriptive Statistics for all Continuous and Dichotomised Variables M ( SD ) Age PSMU LSS Auto Comp Relate Active Use Sex Jobseeker College Age 33.77 ( 8.41 ) PSMU 17.57 ( 3.84 ) − .03 . LS 7.94 ( 1.82 ) .03 .203* Auto 3.21 ( 0.79 ) .02 .198* .167* Comp 3.45 ( 0.71 ) − .009 .197* .216* .415* Relate 3.42 ( 0.75 ) − .004 .225* .195* .373* .478* Active Use 2.72 (0.92) .026 .263* .172*** .053** .067*** .067*** Female - − .061* .001 − .067* .002 .002 .013 − .010 Jobseeker - − .074* .031 − .141* − .041+ − .006 .007 − .036 .128* College - .128* .041 .114* .105* .121* .134* .021 − .037 − .054+ Parent - .399* 0.86 − .089* − .049+ − .074* − .089* .002 − .015 − .093* − .110* Notes. Ns range from 2,668 to 2,536 due to occasional missing data. PSMUS = Positive Social Media Use Scale. LS = Life Satisfaction. Auto = Autonomy. Comp = Competence. Relate = Relatedness. Active use = active/passive social media use *** p < .001, ** p < .01, * p < .05 (two-tailed hypotheses) Regression analysis To further explore the study’s key hypotheses, a series of multivariable linear regression analyses were conducted with each of the wellbeing variables (life satisfaction, autonomy, competence, and relatedness) as the dependent variables and PSMU as the main predictor, while controlling for covariates. With life satisfaction as the dependent variable, a significant model was found, F (4,2560) = 49.44, p < .001, with an R 2 of .071. PSMU was retained as a predictor ( p < .001) with an unstandardized beta coefficient of .075. The covariates, active social media use, education, and parental status were also significant predictors ( p < .001 in all cases), with unstandardized beta coefficients of .242, − .253, and .430, respectively. With autonomy as the dependent variable, a significant model was found, F (4,2560) = 33.38, p < .001, with an R 2 of .050. PSMU was retained as a predictor ( p < .001) with an unstandardized beta coefficient of .039. The covariate education was also retained as a predictor ( p < .001), with an unstandardized beta coefficient of .188. Active social media use and parental status were no longer significant predictors. With competence as the dependent variable, a significant model was found, F (4,2558) = 36.53, p < .001, with an R 2 of .071. PSMU was retained as a predictor ( p < .001) with an unstandardized beta coefficient of .053. The covariates, education, and parental status were also significant predictors ( p < .001 and p = .016), with unstandardized beta coefficients of .185 and − .067, respectively. Active social media use was no longer significant. Finally, with relatedness as the dependent variable, a significant model was found, F (4,2548) = 48.65, p < .001, with an R2 of .071. PSMU was retained as a predictor (p < .001) with an unstandardized beta coefficient of .041. the covariates, education, and parental status were also significant predictors ( p < .001 and p = .004), with unstandardized beta coefficients of .223 and − .084, respectively. Active social media use was no longer significant. PSMU Item-level analysis Examining the individual item-means on the PSMU (see Fig. 1), meaning (item 3) and positive emotions (item 1) were the two most frequently endorsed. All items on the PSMU scale were correlated with LS, with item 4 (Meaning) having the strongest correlation, r (2630) = .182, p < .001. Similarly, all individual PERMA items on the PMUS were positively correlated with Autonomy, Competence, and Relatedness, respectively. In all cases, item 4 (Meaning) was the most strongly correlated, r (2607) = .171, r (2623) = .187, r (2623) = .200. All p values < .001 Country-level differences in PSMU scores To further explore country-level variance in PSMUS scores, a one-way ANOVA was performed to evaluate the relationship between each of the three Gulf Arab States and PSMUS scores. The means and standard deviations are presented in Table 2 Table 2 Descriptive Statistics for PSMU Scores by Country Nation M SD Saudi Arabia 18.16 3.75 UAE 17.39 3.76 Kuwait 17.12 3.96 The ANOVA was significant at the .05 level, F (2, 2558) = 16.869, p < .001. A post hoc Tukey HSD test indicated that the mean PSMUS scores of Saudi Arabia were significantly higher than those of the UAE ( p < .001), and Kuwait ( p < .001). However, there were no significant differences between the mean PSMUS scores of Kuwait and the UAE ( p = .43). Discussion Conducted among residents of three highly connected Gulf Arab States, this study aimed to examine whether self-reported positive social media use (PSMU) correlated with higher levels of life satisfaction and variables believed to underpin subjective wellbeing. This novel measure of positive social media use is based on the PERMA model of wellbeing and explores how individuals perceive their social media use as supporting their experiences of positive emotional states, engagement, relationships, meaning, and achievement. As hypothesised, PSMU was associated with increased life satisfaction, supporting previous research that reports moderate to strong positive relationships between life satisfaction and subjective wellbeing (Berlin & Fors Connolly, 2019). Similarly, PSMU was positively linked with basic psychological needs (autonomy, competence, and relatedness) as described within Self-Determination Theory (Ryan & Deci, 2000 ). The fulfilment of these needs is considered essential for attaining wellbeing, and the correlation with our PERMA-based PSMU measure aligns with this theory. Finally, we also observed the expected correlation between active social media use and PSMU, consistent with prior research suggesting that, under certain conditions, active social media engagement may be related to improved wellbeing (Valkenburg et al., 2022 ). Additionally, multiple regression analysis confirmed that PSMU remained a predictor of all wellbeing variables, even after controlling for active social media use and demographic variables. This finding suggests that, beyond active use, PSMU is a valuable construct for further exploring the relationship between social media and wellbeing. The PERMA framework is deliberately pragmatic (Seligman, 2018 ), and the fact that our PSMU is rooted in PERMA allows us to explore how social media use is associated with the building blocks of wellbeing. Further experimental research is required; however, this approach may offer potential guidance on how to engage with social media in ways that foster human flourishing. For example, positive emotional experiences and engagement (PE) can be encouraged or made more likely by selecting specific platforms and following certain accounts. Similarly, more active use of social media is expected to result from using it to connect with and relate (R) to others, checking in, making positive comments on posts, and cultivating as well as demonstrating more prosocial online behaviours. Interestingly, the PSMU item most strongly correlated with wellbeing was the item related to meaning (M), that is, “My use of social media is well-aligned with my values”. In practical terms, cultivating this aspect of PSMU may involve encouraging self-awareness, value salience and intentional usage, all of which tend to feature in mindfulness-based interventions for problematic technology use (Parisi et al., 2022 ; Thomas et al., 2025 ; Thomas et al., 2024 ). Finally, accomplishment (A) was the lowest-scoring item on the PSMU scale. Boosting the sense of accomplishment one gets from using social media presents a greater challenge, but the answer to this might lie in platform design. We could envisage social media platforms that encourage and facilitate volunteering, allowing people to offer their services to help others, such as answering questions on pre-selected topics. This could also be something like the Be My Eyes application, which connects blind or low-vision users who want assistance with volunteers who, via phone cameras, provide audio or textual descriptions. It is noteworthy that there were minor differences in PSMU between the Gulf Arab States. Specifically, KSA reports a significantly higher frequency of PSMU. One reason for this difference may lie in the population demography of the three states. For instance, the UAE and Kuwait have populations where expatriate or migrant workers are a majority. In KSA, citizens are the majority, and it might be that culture plays a role in PSMU. In the present study, we were unable to explore the impact of the diverse cultures on PSMU. Another possible explanation is the relative popularity of the different social media platforms used in each state. Again, we did not collect this information, but it is conceivable that the preferred platform has an impact on PSMU. For example, platforms like Twitter/X are frequently utilised as a news feed, which can be subject to an amplification bias, where negative emotional language spreads more than its positive equivalent (Schöne & Goldenberg, 2021 ). Overall, the study findings support the idea that social media use can be positively associated with subjective well-being. The PSMU measure was normally distributed, had good internal reliability, and was well correlated with a range of wellbeing metrics among our sample. The study, of course, has several limitations. Firstly, the cross-sectional nature of the study prevents causal or even temporal inferences from being made. The reliance on single-item measures of wellbeing can also be seen as a limitation, given that such measures lack precision, reliability, and do not allow for control of measurement errors. However, the wellbeing measures used in this study have previously been shown to correlate very well with their longer equivalents (Atroszko et al., 2017 ; Martela & Ryan, 2024 ). Future research could address these limitations, employing longitudinal study designs and implementing the long-form versions of the wellbeing measures used. 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Clinical Psychological Science , 7 (3), 462-470. https://doi.org/10.1177/2167702618812727 International Telecommunications Union. (2025). Population coverage, by mobile network technology . ITU. https://datahub.itu.int/data/?i=100095&e=3&s=430 Johannes, N., Vuorre, M., & Przybylski, A. K. (2021). Video game play is positively correlated with well-being. Royal Society Open Science , 8 (2), 202049. https://doi.org/doi:10.1098/rsos.202049 King, D. L., Chamberlain, S. R., Carragher, N., Billieux, J., Stein, D., Mueller, K.,…Delfabbro, P. H. (2020). Screening and assessment tools for gaming disorder: A comprehensive systematic review. Clin Psychol Rev , 77 , 101831. https://doi.org/10.1016/j.cpr.2020.101831 Lindenberg, K., Kindt, S., & Szász-Janocha, C. (2022). Effectiveness of Cognitive Behavioral Therapy-Based Intervention in Preventing Gaming Disorder and Unspecified Internet Use Disorder in Adolescents: A Cluster Randomized Clinical Trial. JAMA Netw Open , 5 (2), e2148995. https://doi.org/10.1001/jamanetworkopen.2021.48995 Martela, F., & Ryan, R. M. (2021). In selecting measures for a comprehensive assessment of well-being, it is essential to include indicators of psychological need satisfaction. Prev Med Rep , 23 , 101474. https://doi.org/10.1016/j.pmedr.2021.101474 Martela, F., & Ryan, R. M. (2024). Assessing autonomy, competence, and relatedness briefly: Validating single-item scales for basic psychological need satisfaction .In [doi:10.1027/1015-5759/a000846]. Hogrefe Publishing. OECD. (2017). How's Life? 2017 Measuring Well-being: Measuring Well-being . OECD Publishing. Omar, B., & Dequan, W. (2020). Watch, Share or Create: The Influence of Personality Traits and User Motivation on TikTok Mobile Video Usage. International Journal of Interactive Mobile Technologies (iJIM) , 14 , 121. https://doi.org/10.3991/ijim.v14i04.12429 Ong, Z. X., Dowthwaite, L., Perez Vallejos, E., Rawsthorne, M., & Long, Y. (2021). Measuring Online Wellbeing: A Scoping Review of Subjective Wellbeing Measures [Review]. Frontiers in Psychology , 12 (511). https://doi.org/10.3389/fpsyg.2021.616637 O’Day, E. B., & Heimberg, R. G. (2021). Social media use, social anxiety, and loneliness: A systematic review. Computers in Human Behavior Reports , 3 , 100070. https://doi.org/https://doi.org/10.1016/j.chbr.2021.100070 Parisi, A., Roberts, R. L., Hanley, A. W., & Garland, E. L. (2022). Mindfulness-Oriented Recovery Enhancement for Addictive Behavior, Psychiatric Distress, and Chronic Pain: A Multilevel Meta-Analysis of Randomized Controlled Trials. Mindfulness (N Y) , 13 (10), 2396-2412. https://doi.org/10.1007/s12671-022-01964-x Pontes, H. M., & Griffiths, M. D. (2015). Measuring DSM-5 internet gaming disorder: Development and validation of a short psychometric scale. Computers in Human Behavior , 45 , 137-143. https://doi.org/https://doi.org/10.1016/j.chb.2014.12.006 Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol , 55 (1), 68-78. https://doi.org/10.1037//0003-066x.55.1.68 Schöne, J., & Goldenberg, A. (2021). Negativity Spreads More than Positivity on Twitter After Both Positive and Negative Political Situations. Affective Science , 2 , 1-12. https://doi.org/10.1007/s42761-021-00057-7 Seligman, M. (2011). Flourish: A visionary new understanding of happiness and well-being . Free Press. Seligman, M. (2018). PERMA and the building blocks of well-being. The Journal of Positive Psychology , 13 (4), 333-335. https://doi.org/10.1080/17439760.2018.1437466 Thomas, J. (2014). Psychological Well-Being in the Gulf States: The New Arabia Felix . Palgrave Macmillan. Thomas, J., Al-Beyahi, F., Colder Carras, M., Aljuboori, D., Alomairah, S., & Thrul, J. (2025). Mindfulness-based approaches to problematic technology use: A scoping review. Computers in Human Behavior Reports , 18 , 100653. https://doi.org/https://doi.org/10.1016/j.chbr.2025.100653 Thomas, J., & AlJedawi, Y. (2025). Positive Social Media Use is Associated with Life Satisfaction, Autonomy, Competence and Relatedness: A Survey Study Spanning 35 Nations. Cyberpsychology Section Annual Conference 2025, STEAMhouse Birmingham City University. https://cms.bps.org.uk/sites/default/files/2025-06/Cyberpsychology%20Section%20Annual%20Conference%202025%20-%20Book%20of%20Abstracts.pdf Thomas, J., AlJuraib, N., Ashour, L., & Karunavira, D. (2024). The Mindfulness-Based Digital Wellbeing Retreat: An Interpretative Phenomenological Analysis. Computers in Human Behaviour Reports , 14 . https://doi.org/https://doi.org/10.1016/j.chbr.2024.100408 Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science , 6 (1), 3-17. https://doi.org/10.1177/2167702617723376 Valkenburg, P. M., van Driel, I. I., & Beyens, I. (2022). The associations of active and passive social media use with well-being: A critical scoping review. New Media & Society , 24 (2), 530-549. https://doi.org/10.1177/14614448211065425 van den Eijnden, R. J. J. M., Lemmens, J. S., & Valkenburg, P. M. (2016). The Social Media Disorder Scale. Computers in Human Behavior , 61 , 478-487. https://doi.org/https://doi.org/10.1016/j.chb.2016.03.038 Verduyn, P., Lee, D. S., Park, J., Shablack, H., Orvell, A., Bayer, J.,…Kross, E. (2015). Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. J Exp Psychol Gen , 144 (2), 480-488. https://doi.org/10.1037/xge0000057 Additional Declarations The authors declare no competing interests. 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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-7051380","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480980935,"identity":"c4760018-4310-401b-82f5-4cba6c7a2f9a","order_by":0,"name":"Justin Thomas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBAC9gYgkcDwn4efnbEBKgShDHBpAapjbEhgYJaRbIZpYSZGC1CZjcFhmBBBLTPSnz94uIONx/gwc5vExz135PiZGRg//GA4bIxbS45hQ+IZHh6zw4xtkjOePTOWbGZgluxhOGyGRwtjQ2KbBEhLszHPgcOJG4AulGZgOGyDx2EPgVoMeIybgVr+QLQw/8anRXBGAtBhbQk8BsyMjY8ZIFrYQLbgdJg0zxvDGYltB3gkDjM2Puw5APILY5tlj0E6Tu/zsac/+Piz7YA9f3v7gwM/DgBDjL358I0fFdaGDbj0oIEDDOC4wh0r2LWMglEwCkbBKEAFALSCVfY+70NJAAAAAElFTkSuQmCC","orcid":"","institution":"King Abdulaziz Center for World Culture (Ithra), Saudi Arabia","correspondingAuthor":true,"prefix":"","firstName":"Justin","middleName":"","lastName":"Thomas","suffix":""},{"id":480980936,"identity":"e15a7470-b4db-49e9-822e-b222dcb1bb0d","order_by":1,"name":"Yasmin Al-Jedawi","email":"","orcid":"","institution":"King Abdulaziz Center for World Culture (Ithra), Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Yasmin","middleName":"","lastName":"Al-Jedawi","suffix":""},{"id":480980937,"identity":"dc7ead18-c70c-4759-b6b1-2a77135ca315","order_by":2,"name":"Fahad Al-Beyahi","email":"","orcid":"","institution":"King Abdulaziz Center for World Culture (Ithra), Saudi Arabia","correspondingAuthor":false,"prefix":"","firstName":"Fahad","middleName":"","lastName":"Al-Beyahi","suffix":""},{"id":480980938,"identity":"85f31dd3-0077-468f-bdcb-4b19a7267998","order_by":3,"name":"Johannes Thrul","email":"","orcid":"","institution":"Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA","correspondingAuthor":false,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Thrul","suffix":""}],"badges":[],"createdAt":"2025-07-05 07:46:18","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7051380/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7051380/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86248540,"identity":"85c9ea0f-ed6f-44c6-bfbd-b333ea3d4d68","added_by":"auto","created_at":"2025-07-08 12:13:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22801,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the mean score for items on the PSMU scale by country\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7051380/v1/208e0ccbbcf61f1da7f25cee.jpg"},{"id":86250063,"identity":"b9a681c8-fd6c-493d-b7e6-69156f4ea50d","added_by":"auto","created_at":"2025-07-08 12:29:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":612554,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7051380/v1/c1939cc9-29f8-4aba-b93c-10b9dd8a8d2a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eE-Arabia Felix: Exploring Positive Social Media Use and Its Association with Wellbeing in the Gulf Arab States\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cb\u003eThe Hyperconnected\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Gulf Arab States, comprising Bahrain, Kuwait, Oman, Qatar, the United Arab Emirates (UAE), and the Kingdom of Saudi Arabia (KSA), have all undergone rapid social, technological, and economic changes throughout the 20th and 21st centuries. Although each state is unique, they also share many commonalities. Along with borders and language (Arabic), they share history, heritage, culture, and religion to varying degrees (Thomas, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). With similar systems of political governance, the Gulf Arab States have all pursued aggressive programs of social, economic, and technological development (al-Khazi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). At the time of writing, all six states have 100% population coverage by mobile internet technology (3G or higher), according to 2023 data reported by the International Telecommunications Union (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, in a survey of 53 nations, Saudi Arabia had the highest rate of social media penetration (the number of active social media users as a percentage of the total population), while the UAE was ranked second (Statista 2025). With widespread high-speed internet access and high levels of social media adoption, the Gulf region is well-positioned to benefit from digital technologies, while also being at the forefront of navigating the challenges of the current and emerging online world.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDigital Illbeing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe notion that digital technology use could become problematic in a psychopathological sense first attracted scientific research attention in the mid-1990s. Drawing parallels with pathological gambling, Griffiths (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) introduced the concept of technological addictions, conceived of as behavioural addictions involving human-computer interactions. It took over two decades for this concept to receive international nosological endorsement; however, in 2018-19, the World Health Organisation (2018) included gaming disorder within the behavioural addictions section of the International Classification of Diseases, 11th Revision (ICD-11). The ICD-11 also enables the diagnosis of other internet-related problems within the section\u0026rsquo;s residual categories, for example, \u0026ldquo;disorders due to addictive behaviours, unspecified\u0026rdquo; (Brand et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lindenberg et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is here that putative technology-related behavioural addictions, such as problematic social media use, could also be diagnosed.\u003c/p\u003e\u003cp\u003eBeyond technological addictions, there is also the contested idea that social media, in particular, is implicated in rising rates of adolescent mental health problems in some nations (Baird et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For example, Twenge et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) linked the rising rates of US adolescent depression and suicide between 2010 and 2015 with the increased use of social media during the same period. Based on survey data for a nationally representative sample of over half a million US adolescents (ages 13 to 18), Twenge et al. found that time spent on social media was positively correlated with mental health issues such as depression. Conversely, time spent on non-screen activities, such as in-person social interaction, sports, and homework, was negatively correlated with mental health issues (Twenge et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOther studies go beyond the rather blunt screen time or social media usage metrics and look more closely at what people are actually doing when they are engaged with these platforms. For example, several studies have differentiated between active (interacting, commenting, posting) and passive social media use, for instance, aimless scrolling through a timeline or repetitively switching between apps (Valkenburg et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Unsurprisingly, there is some evidence that passive use is associated with psychopathology and negative states, while active use is associated with increased wellbeing. For example, several studies have concluded that passive social media use is often preceded by feelings of loneliness (O\u0026rsquo;Day \u0026amp; Heimberg, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, in an experimental lab-based survey, instructing people to use social media passively (rather than actively) led to decreases in subjective wellbeing over time (Verduyn et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In a review of the active-passive social media use literature, Valkenburg et al (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conclude that even the active-passive distinction is too coarse to reliably identify meaningful associations with well-/ill-being, suggesting that future studies need to also consider the content and pre-existing affective state of the user.\u003c/p\u003e\u003cp\u003eWith a few notable exceptions, most of this social media\u0026ndash;pathology research is correlational, telling us little or nothing about causality and possible mechanisms of action. Furthermore, not all of these correlational studies find the posited association between frequency of social media use (screen time), style of social media use (active passive) and psychopathology/illbeing. For example, in a study spanning six years, Heffer et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) report that the frequency of social-media use (hours-per-day) did not predict depressive symptoms among a large US college sample. Similarly, in a cross-sectional study of adolescents, spanning 29 European countries, Boer et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that frequency/intensity of social media use was uncorrelated with psychological wellbeing. In fact, in some nations, those countries where intense social media use was highly prevalent (normalised), the frequency of use was linked to better psychological wellbeing. Similar positive wellbeing associations have also been reported for time spent playing specific online video games (Johannes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe fact that the links between screentime (duration and frequency of social media use) and psychological wellbeing remain equivocal is not to minimise some of the clear harms that are present on these platforms. For example, pro-ana (anorexia nervosa) accounts and social media content romanticising and promoting suicide and self-harm are unequivocally problematic (Baird et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe bulk of psychological research to date exploring the use of digital technologies has focused on deficit states and the potential detrimental health impacts of digital technologies, particularly gaming and social media use. There is, however, also the possibility that social media and gaming can and do contribute to an individual\u0026rsquo;s wellbeing.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDigital Wellbeing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDespite the World Health Organisation\u0026rsquo;s (1948) constitutional affirmation that \"Health is \u0026hellip; not merely the absence of disease or infirmity.\", attempts to measure health and wellbeing are still frequently fixated on assessing the absence of symptoms or deficit states (ill-being). For example, we have numerous measures of smartphone/internet use (Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ghali et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; King et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), social media use (van den Eijnden et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and gaming behaviours (Pontes \u0026amp; Griffiths, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) that focus on pathology. Much of the work undertaken in this area to date has essentially catalogued and explored how personal digital technologies potentially contribute to ill health, psychological disorders, unpleasant, and maladaptive states (e.g., loneliness, social anxiety, hyper-distractibility). Conversely, there are very few, if any, widely used measures assessing positive constructs such as healthy gaming, positive social media use, or satisfaction with online life. We might erroneously conclude that a person has a healthy, wellbeing-promoting, relationship with technology based on their attaining low scores on measures of online pathology. The absence of pathology is not evidence of flourishing. A review, titled \u003cem\u003eOnline Wellbeing\u003c/em\u003e, drew a similar conclusion (Ong et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study identified 56 subjective wellbeing scales; however, none of them were specifically designed to measure online wellbeing or explore how the online world might contribute to an individual\u0026rsquo;s wellbeing.\u003c/p\u003e\u003cp\u003eThis relative gap in the literature suggests the need to explore the presence of digital/online wellbeing through a positive psychology lens. This study examines the concept of positive social media use through the lens of Seligman\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) PERMA model. PERMA proposes five elements (building blocks) that are important for the attainment of subjective wellbeing. Specifically, the components are positive emotions, engagement, relationships, meaning, and achievement/accomplishment. Each of these elements is measurable, can be independent of the others, and is intrinsically motivating, that is, pursued for its own sake (Seligman, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ultimately, the PERMA model is pragmatic, offering a well-articulated framework of measurable components that can serve as targets for interventions aimed at promoting human flourishing.\u003c/p\u003e\u003cp\u003eIn the present study, we use the PERMA model to inform the development of a measure of positive social media use. This self-report scale explores the extent to which an individual\u0026rsquo;s social media use is perceived as making a positive contribution to each of the PERMA components. We focus on citizens and residents of the Gulf Arab States, as these nations all have similarly high rates of social media penetration and widespread access to high-speed mobile internet. In line with the PERMA framework, we hypothesise that positive social media use will be correlated with other measures associated with wellbeing and optimal human functioning, including life satisfaction (OECD, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)and basic psychological needs, specifically autonomy, relatedness, and competence (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These basic psychological needs are identified within Self-Determination Theory (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) as essential for optimal wellbeing and performance. Finally, we also propose that positive social media use will be associated with active social media use, given the previously - albeit inconsistently - observed associations between active use and better wellbeing (Valkenburg et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cb\u003eStudy Design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study is based on a subset of data from our Global Digital Wellbeing Survey (DWS). This was an extensive cross-national study which covered 35 nations. This online study spanned seven world regions and was administered by PSB Insights, a multinational analytics consultancy with substantial experience in cross-national polling and cross-sectional survey research. In this analysis, we extract and focus on the three culturally similar and hyperconnected Gulf Arab States included in the DWS: Kuwait, the United Arab Emirates (UAE), and the Kingdom of Saudi Arabia (KSA). For these Gulf-based participants, survey materials were translated and back-translated from English into Arabic, and made available online in both languages.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Participants in the present study were recruited from pre-existing participant panels. The survey aimed to obtain nationally representative samples (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,000) of the internet-using adult population for each territory included in the DWS. Potential participants (registered panellists) received invitations via email, resulting in an overall response rate of 17.37%. Automated data quality checks ensured that non-completers were excluded, along with respondents who completed the survey with illogical and stereotyped response patterns. Similarly, participants who finished the study at an unfeasible speed were also excluded. The mean exclusion rate was 18.7%. Oversampling ensured each nation comprised approximately 1,000 consenting adult participants. All data were collected between June 12th and July 11th, 2023. The study was reviewed and approved by the King Abdulaziz Centre for World Culture (Ithra) Internal Review Board (IRB 202371). The mean age of participants from the Gulf Arab States was 33.85 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.37). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents additional sample characteristics.\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\u003e\u003cem\u003eSample Characteristics of Respondents from the Participating Gulf States\u003c/em\u003e\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=\"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\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\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,059 (35.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,935 (64.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eJobseeker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e121 (4.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,881 (95.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCollege Graduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,282 (76.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e720 (23.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eParent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,335 (44.47)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,667 (55.52)\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\u003e Of these 3,002 participants, 88.87% (N\u0026thinsp;=\u0026thinsp;2,668) responded affirmatively to a screening question about social media (Twitter, Instagram, TikTok, etc.) use over the past 12 months. Those who answered affirmatively then completed the social media usage measures included in the study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study\u0026rsquo;s posited predictor variable was positive social media use. Our hypotheses suggest this type of social media use will be positively correlated with wellbeing metrics. We also included a measure of active social media use as a suspected covariate of positive social media use and wellbeing. Both of these measures are described below.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePositive Social Media Use (PSMU) Scale\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe PSMU scale is a five-item self-report measure designed to assess positive social media use, grounded in the PERMA model (Thomas \u0026amp; AlJedawi, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). PSMUS was designed to determine how frequently social media is perceived as contributing to wellbeing. The scale is derived from the PERMA-Profiler (Butler \u0026amp; Kern, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which is grounded in Seligman\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) five-dimensional PERMA model of human wellbeing: Positive Emotion (P), Engagement (E), Relationships (R), Meaning (M), and Accomplishment (A). The scale instructs respondents to consider their personal (i.e., non-work-related) use of social media over the past six months when responding to the following PERMA-aligned items:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eUsing social media leaves me feeling positive/joyful/happy. (P)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eI become pleasantly absorbed in what I\u0026rsquo;m doing on social media. (E)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eI feel valued and supported by my social media friends/contacts. (R)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMy use of social media is well-aligned with my values. (M)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSocial media contributes towards my accomplishing goals that are important to me. (A)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eEach PSMU item is frequency-rated on a 4-point scale, from 1 (\u003cem\u003eNever\u003c/em\u003e) to 4 (\u003cem\u003eVery often\u003c/em\u003e). A total score is calculated by summing all five items, with higher scores indicating more positive perceived psychosocial outcomes associated with social media use.\u003c/p\u003e\u003cp\u003e\u003cem\u003eActive/Passive Social Media Use\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe also included a single-item measure of active versus passive social media use. This item asked: \u0026ldquo;Thinking about the past 12 months, how would you describe your use of social media? Please rate on a scale of 1 to 4, where four represents active social media use and one represents passive social media use.\u0026rdquo; We defined passive use as: \u0026ldquo;only reading comments, watching videos and looking at images without further interaction.\u0026rdquo; In contrast, active engagement was described as: \u0026ldquo;making comments, interacting with peers and posting or liking content regularly.\u0026rdquo; Participants indicated if they (1) only use social media passively, (2) use social media more passively than actively, (3) use social media more actively than passively, or (4) only use social media actively.\u003c/p\u003e\u003cp\u003eThe study's dependent variables were measures of life satisfaction and psychological well-being. These four single-item measures are detailed below.\u003c/p\u003e\u003cp\u003e\u003cem\u003eBasic Psychological Need Satisfaction and Frustration (single-item scales)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe Basic Psychological Need Satisfaction and Frustration Scale is grounded in Self Determination Theory, which posits that three basic psychological needs (autonomy, relatedness, and competence) are essential for psychological well-being and optimal human functioning(Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Martela \u0026amp; Ryan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The original BPNSF scale has 12 items (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), four per each psychological need. To create single-item metrics, Martela and Ryan (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) identified the best-performing item for each of the psychological needs: autonomy, relatedness, and competence.\u003c/p\u003e\u003cp\u003eThe performance of these single-item measures was compared to several other longer need satisfaction scales. In all cases, the single items loaded well on the corresponding longer scales and were near identical in their correlation with criterion variables, suggesting that they are valid and useful need satisfaction measures in research contexts requiring brevity (Martela \u0026amp; Ryan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The identified single-item measures and the items used in this study were as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eI feel that my decisions reflect what I really want (Autonomy)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eI feel confident that I can do things well (Competence)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eI feel close and connected with other people who are important to me (Relatedness)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eEach item was scored on a 4-point Likert scale, ranging from \"strongly disagree\" (1) to \"strongly agree\" (4).\u003c/p\u003e\u003cp\u003e\u003cem\u003eOrganisation for Economic Co-operation and Development (OECD) Life Satisfaction\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe OECD\u0026rsquo;s life satisfaction item purports to measure how people evaluate their life as a whole rather than their current feelings. The measure asks respondents to rate their general satisfaction with life on a scale of 0 to 10 (\u0026ldquo;Overall, how satisfied are you with your life?\u0026rdquo;). With greater life satisfaction indicated by higher values, the maximum being 10. The single-item measure was developed as part of the OECD\u0026rsquo;s Better Life Index and has been widely used in their international survey work (OECD, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Atroszko et al (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) Report a highly significant and positive correlation between the single-item life satisfaction measure and the five-item Satisfaction with Life Scale. Similarly, both measures correlated in the same ways with other well-being and personality indicators. Abdel-Khalek (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) reports similarly favourable concurrent validity between multi-item and single-item measures of life satisfaction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch questions and analytic plan\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study seeks to answer four questions: (1) Is PSMU correlated with life satisfaction and existing subjective wellbeing metrics? (2) Is PSMU correlated with active social media use? (3) Does PSMU remain predictive of life satisfaction and wellbeing after controlling for covariates? (4) Do these nations exhibit significant variations in their levels of PSMU? To answer these primary questions, we use correlational and linear regression analyses. The linear regression models involved using each of the wellbeing variables (life satisfaction, autonomy, competence and relatedness) as dependent/criterion variables, while PSMU was the predictor variable. Active social media use and several demographic variables were entered as covariates. To explore between-nation differences, we employed a one-way ANOVA with a post hoc Tukey test.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe data for age, active/passive social media use and positive social media use were normally distributed, while scores for life satisfaction, autonomy, competence, and relatedness were all negatively skewed. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the mean, standard deviation, and correlations for all the study\u0026rsquo;s continuous variables.\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 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eCorrelations and Descriptive Statistics for all Continuous and Dichotomised Variables\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePSMU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLSS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAuto\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eComp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRelate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eActive Use\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eJobseeker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eCollege\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.77 (\u003cem\u003e8.41\u003c/em\u003e)\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSMU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.57 (\u003cem\u003e3.84\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.94 (\u003cem\u003e1.82\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.203*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuto\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.21 (\u003cem\u003e0.79\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.198*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.167*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.45 (\u003cem\u003e0.71\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.197*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.216*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.415*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.42 (\u003cem\u003e0.75\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.225*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.195*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.373*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.478*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActive Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.72 (0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.263*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.172***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.053**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.067***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.067***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\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\u0026minus;\u0026thinsp;.061*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.067*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJobseeker\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\u0026minus;\u0026thinsp;.074*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.141*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.041+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.128*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege\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.128*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.114*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.105*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.121*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.134*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.054+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParent\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.399*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.089*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.049+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.074*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.089*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.093*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.110*\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\u003e\u003cem\u003eNotes. Ns range from 2,668 to 2,536 due to occasional missing data. PSMUS\u0026thinsp;=\u0026thinsp;Positive Social Media Use Scale. LS\u0026thinsp;=\u0026thinsp;Life Satisfaction. Auto\u0026thinsp;=\u0026thinsp;Autonomy. Comp\u0026thinsp;=\u0026thinsp;Competence. Relate\u0026thinsp;=\u0026thinsp;Relatedness. Active use\u0026thinsp;=\u0026thinsp;active/passive social media use\u003c/em\u003e *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05 (two-tailed hypotheses)\u003c/p\u003e\u003cp\u003eRegression analysis\u003c/p\u003e\u003cp\u003eTo further explore the study\u0026rsquo;s key hypotheses, a series of multivariable linear regression analyses were conducted with each of the wellbeing variables (life satisfaction, autonomy, competence, and relatedness) as the dependent variables and PSMU as the main predictor, while controlling for covariates.\u003c/p\u003e\u003cp\u003eWith life satisfaction as the dependent variable, a significant model was found, F (4,2560)\u0026thinsp;=\u0026thinsp;49.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, with an R\u003csup\u003e2\u003c/sup\u003e of .071. PSMU was retained as a predictor (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) with an unstandardized beta coefficient of .075. The covariates, active social media use, education, and parental status were also significant predictors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 in all cases), with unstandardized beta coefficients of .242, \u0026minus;\u0026thinsp;.253, and .430, respectively.\u003c/p\u003e\u003cp\u003eWith autonomy as the dependent variable, a significant model was found, \u003cem\u003eF\u003c/em\u003e (4,2560)\u0026thinsp;=\u0026thinsp;33.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, with an \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of .050. PSMU was retained as a predictor (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) with an unstandardized beta coefficient of .039. The covariate education was also retained as a predictor (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with an unstandardized beta coefficient of .188. Active social media use and parental status were no longer significant predictors.\u003c/p\u003e\u003cp\u003eWith competence as the dependent variable, a significant model was found, \u003cem\u003eF\u003c/em\u003e (4,2558)\u0026thinsp;=\u0026thinsp;36.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, with an \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of .071. PSMU was retained as a predictor (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) with an unstandardized beta coefficient of .053. The covariates, education, and parental status were also significant predictors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.016), with unstandardized beta coefficients of .185 and \u0026minus;\u0026thinsp;.067, respectively. Active social media use was no longer significant.\u003c/p\u003e\u003cp\u003eFinally, with relatedness as the dependent variable, a significant model was found, F (4,2548)\u0026thinsp;=\u0026thinsp;48.65, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, with an R2 of .071. PSMU was retained as a predictor (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) with an unstandardized beta coefficient of .041. the covariates, education, and parental status were also significant predictors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004), with unstandardized beta coefficients of .223 and \u0026minus;\u0026thinsp;.084, respectively. Active social media use was no longer significant.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePSMU Item-level analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eExamining the individual item-means on the PSMU (see Fig.\u0026nbsp;1), meaning (item 3) and positive emotions (item 1) were the two most frequently endorsed.\u003c/p\u003e\u003cp\u003eAll items on the PSMU scale were correlated with LS, with item 4 (Meaning) having the strongest correlation, \u003cem\u003er\u003c/em\u003e (2630)\u0026thinsp;=\u0026thinsp;.182, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. Similarly, all individual PERMA items on the PMUS were positively correlated with Autonomy, Competence, and Relatedness, respectively. In all cases, item 4 (Meaning) was the most strongly correlated, \u003cem\u003er\u003c/em\u003e (2607)\u0026thinsp;=\u0026thinsp;.171, \u003cem\u003er\u003c/em\u003e (2623)\u0026thinsp;=\u0026thinsp;.187, \u003cem\u003er\u003c/em\u003e (2623)\u0026thinsp;=\u0026thinsp;.200. All \u003cem\u003ep\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003cp\u003e\u003cb\u003eCountry-level differences in PSMU scores\u003c/b\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\u003eTo further explore country-level variance in PSMUS scores, a one-way ANOVA was performed to evaluate the relationship between each of the three Gulf Arab States and PSMUS scores. The means and standard deviations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eDescriptive Statistics for PSMU Scores by Country\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKuwait\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.96\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 ANOVA was significant at the .05 level, \u003cem\u003eF\u003c/em\u003e (2, 2558)\u0026thinsp;=\u0026thinsp;16.869, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. A post hoc Tukey HSD test indicated that the mean PSMUS scores of Saudi Arabia were significantly higher than those of the UAE (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and Kuwait (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). However, there were no significant differences between the mean PSMUS scores of Kuwait and the UAE (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.43).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eConducted among residents of three highly connected Gulf Arab States, this study aimed to examine whether self-reported positive social media use (PSMU) correlated with higher levels of life satisfaction and variables believed to underpin subjective wellbeing. This novel measure of positive social media use is based on the PERMA model of wellbeing and explores how individuals perceive their social media use as supporting their experiences of positive emotional states, engagement, relationships, meaning, and achievement. As hypothesised, PSMU was associated with increased life satisfaction, supporting previous research that reports moderate to strong positive relationships between life satisfaction and subjective wellbeing (Berlin \u0026amp; Fors Connolly, 2019). Similarly, PSMU was positively linked with basic psychological needs (autonomy, competence, and relatedness) as described within Self-Determination Theory (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The fulfilment of these needs is considered essential for attaining wellbeing, and the correlation with our PERMA-based PSMU measure aligns with this theory. Finally, we also observed the expected correlation between active social media use and PSMU, consistent with prior research suggesting that, under certain conditions, active social media engagement may be related to improved wellbeing (Valkenburg et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, multiple regression analysis confirmed that PSMU remained a predictor of all wellbeing variables, even after controlling for active social media use and demographic variables. This finding suggests that, beyond active use, PSMU is a valuable construct for further exploring the relationship between social media and wellbeing.\u003c/p\u003e\u003cp\u003eThe PERMA framework is deliberately pragmatic (Seligman, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the fact that our PSMU is rooted in PERMA allows us to explore how social media use is associated with the building blocks of wellbeing. Further experimental research is required; however, this approach may offer potential guidance on how to engage with social media in ways that foster human flourishing. For example, positive emotional experiences and engagement (PE) can be encouraged or made more likely by selecting specific platforms and following certain accounts. Similarly, more active use of social media is expected to result from using it to connect with and relate (R) to others, checking in, making positive comments on posts, and cultivating as well as demonstrating more prosocial online behaviours. Interestingly, the PSMU item most strongly correlated with wellbeing was the item related to meaning (M), that is, \u0026ldquo;My use of social media is well-aligned with my values\u0026rdquo;. In practical terms, cultivating this aspect of PSMU may involve encouraging self-awareness, value salience and intentional usage, all of which tend to feature in mindfulness-based interventions for problematic technology use (Parisi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Finally, accomplishment (A) was the lowest-scoring item on the PSMU scale. Boosting the sense of accomplishment one gets from using social media presents a greater challenge, but the answer to this might lie in platform design. We could envisage social media platforms that encourage and facilitate volunteering, allowing people to offer their services to help others, such as answering questions on pre-selected topics. This could also be something like the Be My Eyes application, which connects blind or low-vision users who want assistance with volunteers who, via phone cameras, provide audio or textual descriptions.\u003c/p\u003e\u003cp\u003eIt is noteworthy that there were minor differences in PSMU between the Gulf Arab States. Specifically, KSA reports a significantly higher frequency of PSMU. One reason for this difference may lie in the population demography of the three states. For instance, the UAE and Kuwait have populations where expatriate or migrant workers are a majority. In KSA, citizens are the majority, and it might be that culture plays a role in PSMU. In the present study, we were unable to explore the impact of the diverse cultures on PSMU. Another possible explanation is the relative popularity of the different social media platforms used in each state. Again, we did not collect this information, but it is conceivable that the preferred platform has an impact on PSMU. For example, platforms like Twitter/X are frequently utilised as a news feed, which can be subject to an amplification bias, where negative emotional language spreads more than its positive equivalent (Sch\u0026ouml;ne \u0026amp; Goldenberg, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, the study findings support the idea that social media use can be positively associated with subjective well-being. The PSMU measure was normally distributed, had good internal reliability, and was well correlated with a range of wellbeing metrics among our sample. The study, of course, has several limitations. Firstly, the cross-sectional nature of the study prevents causal or even temporal inferences from being made. The reliance on single-item measures of wellbeing can also be seen as a limitation, given that such measures lack precision, reliability, and do not allow for control of measurement errors. However, the wellbeing measures used in this study have previously been shown to correlate very well with their longer equivalents (Atroszko et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Martela \u0026amp; Ryan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFuture research could address these limitations, employing longitudinal study designs and implementing the long-form versions of the wellbeing measures used. Additionally, experimental studies could be designed where individuals are coached in positive social media use compared to a control condition, using state-based wellbeing metrics as the outcome. Rather than focusing on the question of whether social media is a net benefit or deficit, it seems wiser to identify ways in which we can best use (and design) such platforms to promote human flourishing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdel-Khalek, A. (2006). Measuring Happiness with a Single-Item Scale. \u003cem\u003eSocial Behavior and Personality: an international journal\u003c/em\u003e,\u003cem\u003e 34\u003c/em\u003e, 139-150. https://doi.org/10.2224/sbp.2006.34.2.139 \u003c/li\u003e\n\u003cli\u003eal-Khazi, L. A. (2008). Gulf Societies: Coexistence of Tradition and Modernity. In A. Alsharekh \u0026amp; R. Sprinborg (Eds.), \u003cem\u003ePopular Culture and Political Identity in the Arab Gulf States\u003c/em\u003e. SAQI in association with The London Middle East Institute at SOAS. \u003c/li\u003e\n\u003cli\u003eAtroszko, P., Sawicki, A., Mąkinia, A., \u0026amp; Atroszko, B. 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The Social Media Disorder Scale. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e,\u003cem\u003e 61\u003c/em\u003e, 478-487. https://doi.org/https://doi.org/10.1016/j.chb.2016.03.038 \u003c/li\u003e\n\u003cli\u003eVerduyn, P., Lee, D. S., Park, J., Shablack, H., Orvell, A., Bayer, J.,\u0026hellip;Kross, E. (2015). Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. \u003cem\u003eJ Exp Psychol Gen\u003c/em\u003e,\u003cem\u003e 144\u003c/em\u003e(2), 480-488. https://doi.org/10.1037/xge0000057 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"King Abdulaziz Center for World Culture (Ithra)","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PERMA, Social Media, Arab, Internet, Wellbeing","lastPublishedDoi":"10.21203/rs.3.rs-7051380/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7051380/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocial media penetration rates (the number of active social media users as a percentage of the total population) are notably high among the Gulf Arab States. Most studies exploring the wellbeing implications of social media have focused on deficit states and problematic use, frequently examining posited links with psychopathology, loneliness, and broader health concerns. Fewer studies attempt to explore how social media use might also contribute to subjective wellbeing. Grounded in positive psychology, this study examined the relationship between positive social media use (PSMU) and wellbeing. Derived from a large-scale cross-national survey, focusing on three Gulf Arab States (UAE, Saudi Arabia, and Kuwait), participants (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3,002) completed a novel five-item measure of positive social media use, assessing the extent to which social media was perceived as contributing to positive emotions, engagement, relationships, meaning and accomplishment (PERMA). Participants also completed single-item measures of active social media use, life satisfaction, autonomy, competence, and relatedness. PSMU was correlated with life satisfaction, autonomy, competence, and relatedness. A regression analysis revealed that the association between positive social media use and wellbeing variables persisted after controlling for active use and demographic covariates. The results are discussed in the context of exploring how social media use might make positive contributions to wellbeing.\u003c/p\u003e","manuscriptTitle":"E-Arabia Felix: Exploring Positive Social Media Use and Its Association with Wellbeing in the Gulf Arab States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 12:13:28","doi":"10.21203/rs.3.rs-7051380/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11d6c192-58df-46c5-84b0-53e6e94a258a","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51079144,"name":"Psychology"}],"tags":[],"updatedAt":"2025-07-08T12:13:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 12:13:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7051380","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7051380","identity":"rs-7051380","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}