Using digital photovoice to explore the relationships between social media content and suicidality among transgender adolescents

Project Summary/Abstract Social media has a complex influence on mental health. It can contribute to bullying and harassment, which may result in loneliness, depression, and suicidality, but it can also be helpful, by fostering social connections and offering resources; these influences may both harm and benefit the same individuals. Transgender youth

is a highly vulnerable group, who is often subjected to bullying and are far higher risk for self-harm and suicidality than cisgender youth. An increasing number of trans adolescents are using social media. In this proposed study, we intend to understand the effect of emotionally laden social media content consumed by

trans and gender non-binary adolescents (TGNB) (ages 15 - 20) and its connection to suicidality. We focus on high schoolers and emerging adults, as suicidality rates in this group of social media users is higher than among middle-schooler social media users. Existing research is largely cross-sectional, correlational,

retrospective, and focuses on “posts” (content individuals produce themselves), which limits its utility in understanding fully the impact of social media on adolescents, for three reasons. First, most social media use consists of passive consumption, not active posting. Second, it is important to gain in-the-moment information

about emotions individuals experience when a particularly salient social media content is consumed. Finally, retrospective and correlational designs lack the capacity to generate or test predictive models. To address these gaps, our study will be prospective and longitudinal, focusing on passive social media consumption, and

deeply individualized and person-centered. We will adapt `Photovoice' (PV), a commonly-used community- based participatory research method that is considered an excellent method for understanding the perspectives of individuals, including those from underrepresented populations. We will use a previously

piloted S.T.A.R (Screen Tag and React) app, with which participants will be able to capture screenshots of anything they see on any social media platform, indicate their emotional reaction to that content, and upload these on a secure cloud server. Participants will be recruited largely from social media groups for TGNB

populations and their parents. Participants will use the S.T.A.R. app for 8 weeks, undergo an interview to better understand the images they capture, and then use it for another 8 weeks so that prediction models we develop could be evaluated. Thus, with this study, we will be able to understand whether, for each TGNB adolescent,

the unique patterns of positive/negative emotions elicited by social media are associated with patterns in weekly assessment of suicidality. We will also be able to determine whether specific types of social media content are associated with changes in suicidality scores for transgender adolescents. Finally, we will be able

to create a prediction model to map social media content types and related emotions to predict suicidality.