Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.Research 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
(F1 = 0.798, precision = 0.806, recall = 0.807). Last but not least, we develop an online
platform to allow Twitter users to probe their profiles for signs of depression. By doing
so, we hope to empower users to better understand their signals and to steer them to seek
professional assistance whenever needed
