From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses

By Glen A. Coppersmith, Mark Dredze, Craig Harman, Kristy Hollingshead. CLPsych@HLT-NAACL, 2015

329 citations recorded by the source corpus.

Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Language provides a natural lens for studying mental health ‐ much existing work and therapy have strong linguistic components, so the creation of a large, varied, language-centric dataset could provide significant grist for the field of mental health research. We examine a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis. We systematically explore language differences between ten conditions with respect to the general population, and to each other. Our aim is to provide guidance and a roadmap for where deeper exploration is likely to be fruitful.

Subfields: Applied Psychology, Artificial Intelligence, Social Psychology

View Paper: https://www.semanticscholar.org/paper/49d271c1cb6b0f11c1495567e8c13dacd854f589

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Citations in the corpus

Detecting depression and mental illness on social media: an integrative review (2017, background): “Other studies have used psychological dictionaries (Linguistic Inquiry and Word Count; LIWC) [20] to characterize differences between mental illness conditions [21], with some success.”
Detection of Depression-Related Posts in Reddit Social Media Forum (2019, background): “[6] looked for tweets that explicitly stated ‘‘I was just diagnosed with depression’’ sentences.”
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media (2016, unknown): “Linguistic attributes of shared content and social interactional patterns have been utilized to understand and infer risk to major depressive disorder [24, 49, 32, 16, 60, 70], postpartum depression [21, 22], addiction [47, 44], and other mental health concerns [35, 18, 17, 46].”
Depression and Self-Harm Risk Assessment in Online Forums (2017, methodology): “This assessment is supported by previous work on the ReachOut dataset (Milne et al., 2016), on Twitter data (Coppersmith et al., 2015b), and on other Reddit data (Losada and Crestani, 2016).”
Deep Learning for Depression Detection of Twitter Users (2018, methodology): “…extraction mechanism in detecting mental illnesses such as attention deﬁcit hyperactivity disorder (ADHD), generalized anxiety disorder, and eight other mental illnesses (Coppersmith et al., 2015a) as well as in detecting rare mental health conditions such as schizophrenia (Mitchell et al., 2015).”
Leveraging Large Language Models for Mental Health Prediction via Online Text Data (2023, background): “In the past decade, there has been extensive research about leveraging content analysis and social interaction patterns to identify and predict risks associated with mental health issues, such as anxiety [6, 72, 77], major depressive disorder [24, 26, 61, 83], suicide ideation [13, 21, 27, 71, 80], ”