Searching Brazilian Twitter for Signs of Mental Health Issues

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 6111–6117 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 6111 Searching Brazilian Twitter for signs of mental health issues Wesley Ramos dos Santos, Amanda Maria Martins Funabashi, Ivandr´e Paraboni School of Arts, Sciences and Humanities (EACH) University of S˜ao Paulo (USP) {wesley.ramos,amanda.funabashi,ivandre}@usp.br Abstract Depression and related mental health issues are often reflected in the language employed by the individuals who suffer from these conditions and, accordingly, research in Natural Language Processing (NLP) and related fields have developed an increasing number of studies devoted to their recognition in social media text. Some of these studies have also attempted to go beyond recognition by focusing on the early signs of these illnesses, and by analysing the users’ publication history over time to potentially prevent further harm. The two kinds of study are of course overlapping, and often make use of supervised machine learning methods based on annotated corpora. However, as in many other fields, existing resources are largely devoted to English NLP, and there is little support for these studies in under resourced languages. To bridge this gap, in this paper we describe the initial steps towards building a novel resource of this kind - a corpus intended to support both the recognition of mental health issues and the temporal analysis of these illnesses - in the Brazilian Portuguese language, and initial results of a number of experiments in text classification addressing both tasks. Keywords: text classification, mental health, depression 1. Introduction Depression and related mental health issues are well-known challenges of modern life, and are often reflected in the language employed by the individuals who suffer from these conditions. Accordingly, research in Natural Lan- guage Processing (NLP) and related fields have developed an increasing number of studies devoted to the computa- tional recognition of depression, anxiety, bipolar disorder, anorexia, suicidal thought and many others from social me- dia text (usually Twitter or Reddit) (Resnik et al., 2013; Resnik et al., 2015; Jamil et al., 2017; Yates et al., 2017; Orabi et al., 2018). In addition to these, a second line of research has recently attempted to go beyond recogni- tion by focusing on the early signs of these illnesses, and by analysing the users’ publication history over time to potentially anticipate treatment and prevent further harm (Trotzek et al., 2018b; Losada et al., 2019). The two kinds of study are of course overlapping, and usually make use of supervised machine learning meth- ods based on annotated corpora (Coppersmith et al., 2015; Losada et al., 2017; Yates et al., 2017). As in many other fields, however, existing resources are largely devoted to English NLP, and there is little support for studies of this kind in under resourced languages. To help bridge this gap, this paper describes the initial steps towards building a novel language resource of this kind, namely, a Brazilian Portuguese corpus of tweets written by users with a diagnosed mental health issue. …

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