Mining Twitter Data for Signs of Depression in Brazil - von Sperling, Otto; Ladeira, Marcelo (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), 2019)
The literature on computerized models that help detect, study and understand signs of mental health disorders from social media has been thriving since the mid-2000s for English speakers. In Brazil, this area of research shows promising results, in addition to a variety of niches that still need exploring. Thus, we construct a large corpus from 2941 users (1486 depressive, 1455 non-depressive), and induce machine learning models to identify signs of depression from our Twitter corpus. In order to achieve our goal, we extract features by measuring linguistic style, behavioral patterns, and affect from users’ public tweets and metadata. Resulting models successfully distinguish between depressive and non-depressive classes with performance scores comparable to results in the literature. We hope that our findings can become stepping stones towards more methodologies being applied at the service of mental health.