Automatic analysis of X (Twitter) data for supporting depression diagnosis

Abstract

Depression is an increasingly common problem that often goes undiagnosed. The aim of this paper was to determine whether an analysis of tweets can serve as a proxy for assessing depression levels in the society. The work considered keyword-based sentiment analysis, which was enhanced to exclude informational tweets about depression or about recovery. The results demonstrated the words used in the posts most often and the emotional polarity of the tweets. A schedule of user activity was mapped out and trends related to daily activity of users were analyzed. It was observed that the identified X (Twitter) activity related to depression corresponded well with reports on persons with depression and statistics related to suicidal deaths. Therefore, it could be construed that people with undiagnosed depression express their feelings in social media more often, looking, in this way, for help with their emotional problems.