“Because... I was told... so much”: Linguistic Indicators of Mental Health Status on Twitter

Abstract

Recent studies have shown that machine learning can identify individuals with mental illnesses by analyzing their social media posts. Topics and words related to mental health are some of the top predictors. These findings have implications for early detection of mental illnesses. However, they also raise numerous privacy concerns. To fully evaluate the implications for privacy, we analyze the performance of different machine learning models in the absence of tweets that talk about mental illnesses. Our results show that machine learning can be used to make predictions even if the users do not actively talk about their mental illness. To fully understand the implications of these findings, we analyze the features that make these predictions possible. We analyze bag-of-words, word clusters, part of speech n-gram features, and topic models to understand the machine learning model and to discover language patterns that differentiate individuals with mental illnesses from a control group. This analysis confirmed some of the known language patterns and uncovered several new patterns. We then discuss the possible applications of machine learning to identify mental illnesses, the feasibility of such applications, associated privacy implications, and analyze the feasibility of potential mitigations.

Social media use for supporting mental health (SMILE)

“…Much research has been carried out over the years, considering Facebook as a medium of support for mental health (Frost and Rickwood, 2017; Kayrouz et al , 2016; Nisar et al , 2019; Mikal et al , 2020). Within the past year, numerous studies have also turned to Twitter to study linguistic indicators of and support seeking for, mental health (Guntuku et al , 2019a; Weerasinghe et al , 2019; Sasso et al , 2019; Guntuku et al , 2019b).…”

Predicting Mental Health Problems with Automatic Identification of Metaphors

“…Ramiandrisoa et al [ 40 ] tried a variety of lexical features in another evaluation task on the CLEF 2018 eRisk database [ 41 ], including bag of word models, specific category words, and special word combinations, and they converted text into vectors for classification. Weerasinghe et al [ 42 ] investigated language patterns that differentiate individuals with mental illnesses from a control group, including bag-of-words, word clusters, part of speech n-gram features, and topic models to understand the machine learning model.…”

“…[41], including bag of word models, specific category words, and special word combinations, and they converted text into vectors for classification. Weerasinghe et al [42] investigated language patterns that differentiate individuals with mental illnesses from a control group, including bag-of-words, word clusters, part of speech n-gram features, and topic models to understand the machine learning model. In addition to the use of text and other user characteristics, the rise of deep learning has provided new ways to detect mental illness through text.…”

Análise e Previsão do Tom Emocional de Usuários em Comunidades de Saúde Mental no Reddit

“…Os autores mostram que o método proposto, que utiliza características psicolinguísticas,é eficiente para realizar a classificação dos usuários com depressão. Em outro trabalho, os autores em [Weerasinghe et al 2019] analisaram os tópicos no Twitter para descobrir padrões de linguagem que diferenciam indivíduos com doenças mentais de um grupo de controle. Como resultado, os pesquisadores confirmaram certos padrões e descobriram outros novos.…”