Quantifying Mental Health Signals in Twitter

By Glen A. Coppersmith, Mark Dredze, Craig Harman. 2014

729 citations recorded by the source corpus.

The ubiquity of social media provides a rich opportunity to enhance the data available to mental health clinicians and researchers, enabling a better-informed and better-equipped mental health field. We present analysis of mental health phenomena in publicly available Twitter data, demonstrating how rigorous application of simple natural language processing methods can yield insight into specific disorders as well as mental health writ large, along with evidence that as-of-yet undiscovered linguistic signals relevant to mental health exist in social media. We present a novel method for gathering data for a range of mental illnesses quickly and cheaply, then focus on analysis of four in particular: post-traumatic stress disorder (PTSD), depression, bipolar disorder, and seasonal affective disorder (SAD). We intend for these proof-of-concept results to inform the necessary ethical discussion regarding the balance between the utility of such data and the privacy of mental health related information.

Subfields: Applied Psychology, Social Psychology, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “Intentions of self harm Amongst the large body of literature on depression detection and mental health assessment in social media (e.g., Benton et al., 2017; Coppersmith et al., 2014; De Choudhury et al., 2013, inter alia), some research focuses on detecting risk of self-harm.”
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems (2022, background): “Intentions of self harm Amongst the large body of work on mental health assessment in social media (e.g., Benton et al., 2017; Coppersmith et al., 2014; De Choudhury et al., 2013, inter alia), some research focuses on detecting risk of self-harm.”
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media (2016, neutral): “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].”
The role of personality, age, and gender in tweeting about mental illness (2015, methodology): “As Coppersmith et al. (2014b) acknowledge, this method of collection is susceptible to multiple biases, but represents a simple way to build a large dataset of users and their textual information.”
Dynamic Wordclouds and Vennclouds for Exploratory Data Analysis (2014, methodology): “Further, some linguistic psychometric analysis depends on the relative distribution of pronouns and other seemingly contentless words (Coppersmith et al., 2014a; Chung and Pen-nebaker, 2007).”
Studying User Income through Language, Behaviour and Affect in Social Media (2015, methodology): “Notably, the self-identification approach is being used widely throughout the literature [58–60].”
Detection of Depression-Related Posts in Reddit Social Media Forum (2019, background): “It is often presented in several mental health projects [6], [11], [12], [45]–[48].”
A meta-analysis of correlations between depression and first person singular pronoun use (2017, background): “Thus, it makes sense to use language as a marker of whether people are depressed (Coppersmith et al., 2014).”
Depression and Self-Harm Risk Assessment in Online Forums (2017, background): “This makes social media a significant resource for studying language related to depression, suicide, and self-harm, as well as understanding the authors’ reasons for making such posts, and identifying individuals at risk of harm (Coppersmith et al., 2014a).”
Analyzing Biases in Human Perception of User Age and Gender from Text (2016, background): “…orientation (Volkova et al., 2014), income (Preot¸iuc-Pietro et al., 2015c), socio-economic status (Lam-pos et al., 2016), popularity (Lampos et al., 2014), personality (Schwartz et al., 2013) or mental illnesses (De Choudhury et al., 2013; Coppersmith et al., 2014; Preot¸iuc-Pietro et al., 2015a).”
Returning the N to NLP: Towards Contextually Personalized Classification Models (2020, background): “…2010), political preferences (Volkova et al., 2014), socio-economic status (Preot¸iuc-Pietro et al., 2015), impact (Lampos et al., 2014), and a range of psychological traits and issues (Schwartz et al., 2013; Park et al., 2015; Sum-ner et al., 2012; Guntuku et al., 2017; Coppersmith et al., 2014).”
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task (2021, methodology): “As two particularly well known and inﬂuential examples, Coppersmith et al. (2014) infer mental health diagnoses of Twitter users by looking for publicly self-reported diagnoses, and De Choudhury et al. (2016) infer mental health progressions to suicidal ideation by examining when Reddit users shift…”