Sociological Analysis of Gender Differences in Suicide Ideation on Instagram: A Social Network Analysis Approach Using Big Data - Somayeh Mirzaee; Akbar Aliverdinia (جامعه شناسی کاربردی)
IntroductionOfficial statistics indicate that, while the global suicide rate has declined over the past twenty years, Iran has experienced an alarming increase of over 44%. Suicidal ideation is a significant risk factor for suicide. Research has demonstrated that social media relationships can influence the dissemination of suicidal thoughts and behaviors. Despite observable differences in how men and women use social media, as well as mixed findings regarding gender differences in suicidal ideation, there remains a gap in understanding these issues from a sociological perspective that focuses on communication network structures. The interconnected nature of social media facilitates network analysis through big data. Given the rising prevalence of social media usage, this study aimed to provide a sociological analysis of gender differences in suicidal ideation on Instagram. It will leverage Krohn’s network theory and its evolution, employing big data and social network analysis, while also offering policy implications.  Materials & MethodsThis study employed an exploratory-quantitative research method, utilizing network analysis with big data sourced from Instagram. The research sample comprised Instagram users, who had made at least 4 posts featuring hashtags related to suicidal ideation, totaling 957 users, of whom 514 had public and accessible accounts. Data collection occurred in 3 phases: Phase 1: Identification of the research sample through posts containing hashtags related to suicidal ideation, resulting in a dataset of 20,223 posts. Phase 2: Compilation of the follower and following lists of the research sample to construct the relationship network, yielding 2,037,883 nodes and 2,269,856 edges. Phase 3: A repeat of the first phase after 1 year to assess changes in the level of suicidal ideation among the research sample, resulting in an additional 8,913 posts. During data collection, the study utilized the Dataak platform, Python, and Ninjagram software. For data cleaning, Excel was employed, while Gephi and R software were used for data analysis to calculate network variables. The hypotheses were tested using SPSS software. In line with the primary research question, the unit of observation included the posts and accounts of Instagram users, the unit of analysis was the individual (Instagram users), and the level of analysis was micro-level.  Discussion of Results & ConclusionThe results of the logistic regression analysis revealed that, in terms of their influence on the dependent variable, the following variables were significant: for women, exposure to suicidal ideation, prior suicidal ideation, out-coreness centrality, and closeness centrality; for men, prior suicidal ideation, in-coreness centrality, and intensity (reciprocity); and for the overall sample, prior suicidal ideation, intensity (reciprocity), and exposure to suicidal ideation, all of which had a direct and significant impact on suicidal ideation. The explanatory power of the model was notably higher for women (53 to 31.2%) compared to men (28.3 to 18.2%) and the overall sample (28.3 to 17.9%). These findings suggested that, although there was no significant difference in the prevalence or likelihood of suicidal ideation between women and men in the sample, the mechanisms, by which an individual's position in the communication network surrounding suicidal thoughts and behaviors influenced the likelihood of suicidal ideation, differed by gender. Consequently, it is recommended that preventive interventions be designed with gender considerations in mind. Drawing on the findings of this research, policy implications based on Krohn’s theory and its development at micro, medium, and macro levels are proposed. These interventions aim to reduce the centrality of users within communication networks focused on suicidal ideas, attitudes, and norms.