Exploring depressive moods through the lens of online social behaviors

Abstract

As people around the world are spending increasing amounts of time online, the question of how to make online experiences more pleasant and rewarding, and beneficial for mental health and well-being becomes essential. The research goal of this thesis is to understand the relationship between online activities on Facebook and a common mental disorder, depression.

Based on a 234 participant study with two times of experiments by utilizing the web-based application developed, first, it was revealed that certain Facebook activities had predictive power in distinguishing depressed and non-depressed individuals. Participants` responses to the pages of tips and facts related to depression, which can be explained by the number of checked app-tips and app-points, had a positive correlation whereas the number of geotags had a negative correlation with the CES-D scale. Furthermore, in finding group differences in Facebook social activities, checked app-tips and app-points resulted in significant differences between probably depressed and non-depressed individuals.

Second, further investigation was conducted and it was found that not only the sheer size of the friends network had predictive power on depression but also the frequency and diversity of online interactions had high association with depression. Comments had a strong association with depression as well as likes, which is a popular positive lightweight interaction. Although depressed individuals reported qualitatively and quantitatively smaller involved online social network with whom they interacted they showed the similar degree of attention to the involved friends within their small networks compared to non-depressed individuals. Yet, they mainly showed the one-way communication style such as clicking likes on fan pages or uploading texts on their walls at Facebook; these kinds of one way communication exposing themselves to outside may be linked with loneliness.

The application could successfully evaluate...