The role of personality, age, and gender in tweeting about mental illness

By Daniel Preotiuc-Pietro, Johannes C. Eichstaedt, Gregory J. Park, Maarten Sap, Laura K Smith, Victoria Tobolsky, H. A. Schwartz, Lyle Ungar. 2015

166 citations recorded by the source corpus.

Mental illnesses, such as depression and post traumatic stress disorder (PTSD), are highly underdiagnosed globally. Populations sharing similar demographics and personality traits are known to be more at risk than others. In this study, we characterise the language use of users disclosing their mental illness on Twitter. Language-derived personality and demographic estimates show surprisingly strong performance in distinguishing users that tweet a diagnosis of depression or PTSD from random controls, reaching an area under the receiveroperating characteristic curve ‐ AUC ‐ of around .8 in all our binary classification tasks. In fact, when distinguishing users disclosing depression from those disclosing PTSD, the single feature of estimated age shows nearly as strong performance (AUC = .806) as using thousands of topics (AUC = .819) or tens of thousands of n-grams (AUC = .812). We also find that differential language analyses, controlled for demographics, recover many symptoms associated with the mental illnesses in the clinical literature.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Social Psychology

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Citations in the corpus

Detection of Depression-Related Posts in Reddit Social Media Forum (2019, methodology): “Some other papers compare the performance of individual features with various machine learning classifiers [9]–[12].”
Multitask Learning for Mental Health Conditions with Limited Social Media Data (2017, background): “…et al., 2014), gender (Sarawgi et al., 2011; Ciot et al., 2013; Liu and Ruths, 2013; Volkova et al., 2015; Hovy, 2015), personality (Schwartz et al., 2013; Volkova et al., 2014; Plank and Hovy, 2015; Park et al., 2015; Preot¸iuc-Pietro et al., 2015), and occupation (Preotiuc-Pietro et al., 2015).”
Ethical Research Protocols for Social Media Health Research (2017, background): “A research community has grown around using social media data to assess and understand mental health (Resnik et al., 2013; Schwartz et al., 2013; Preotiuc-Pietro et al., 2015; Coppersmith et al., 2015a; De Choudhury et al., 2016).”
Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network (2016, methodology): “Another study used these vocabularies to propose a feature related to age and gender to detect mental illnesses, such as depression and post-traumatic stress disorder, on Twitter (Preotiuc-Pietro et al., 2015).”