- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Online Social Networking Sites (SNSs) are immensely popular, especially among adolescents. Activity on these sites leaves digital footprints, which may be used to study online behavioral correlates of adolescent psychological distress and to, ultimately, improve detection and intervention efforts. In the present work, we explore the digital footprints of adolescent depression, social rejection, and victimization of bullying on Facebook. Two consecutive studies were conducted among Israeli adolescents (N = 86 and N = 162). We collected a range of Facebook activity features, as well as self-report measurements of depression, social rejection, and victimization of bullying. Findings from Study 1 demonstrate that explicit distress references in Facebook postings (e.g., "Life sucks, I want to die") predict depression among adolescents, but that such explicit distress references are rare. In Study 2, we applied a bottom-up research methodology along with the previous top-down, theory driven approach. Study 2 demonstrates that less explicit features of Facebook behavior predict social rejection and victimization of bullying. These features include 'posts by others', 'check-ins', 'gothic and dark content', 'other people in pictures', and 'positive attitudes towards others'. The potential, promises and limitations of using digital Facebook footprints for the detection of adolescent psychological distress are discussed.
The take-home message of the research presented here can be concluded as follows: (a) adolescent postings of explicit distress in Facebook are rare; (b) when they do appear, they usually include references to depressive symptoms and they are indeed predictive of depression among adolescents; and (c) psychosocial distress (i.e., negative interpersonal experiences of social rejection and victimization of bullying) leaves less explicit, digital footprints that can be discerned and detected. We contend that these results should be seen as first illustrations for the new opportunities generated with the rise of social media. Further research is required, preferably in larger samples and with automated language processing tools to prove the proposed direction of the research and reveal a more comprehensive picture of how distress is expressed in online behavior. Scholars that have already used such computerized tools to predict personal features such as age, gender, or introversion (Schwartz et al., 2013) are predicting that early screening of mental health conditions from social media activity logs is right around the corner (Csepeli & Nagyfi, 2017; Park et al., 2014). The findings presented here show this seems a viable and realistic expectation, and the next step in this line of research.