Measuring Post Traumatic Stress Disorder in Twitter

Glen Coppersmith · Craig Harman · Mark H. Dredze

316 citations0/0 files storedProceedings of the International AAAI Conference on Web and Social Media

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Mental Health via Writing Posttraumatic Stress Disorder Research Sentiment Analysis and Opinion Mining

Abstract

Traditional mental health studies rely on information primarily collected through personal contact with a health care professional. Recent work has shown the utility of social media data for studying depression, but there have been limited evaluations of other mental health conditions. We consider post traumatic stress disorder (PTSD), a serious condition that affects millions worldwide, with especially high rates in military veterans. We also present a novel method to obtain a PTSD classifier for social media using simple searches of available Twitter data, a significant reduction in training data cost compared to previous work. We demonstrate its utility by examining differences in language use between PTSD and random individuals, building classifiers to separate these two groups and by detecting elevated rates of PTSD at and around U.S. military bases using our classifiers.

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2015The Development and Psychometric Properties of LIWC2015 2021Predicting Depression via Social Media 2010Social network determinants of depression 2013Predicting postpartum changes in emotion and behavior via social media

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2024Mental-LLM 2020Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features 2017Detecting Stress Based on Social Interactions in Social Networks 2020Application of Machine Learning Methods in Mental Health Detection: A Systematic Review 2017Gender and Cross-Cultural Differences in Social Media Disclosures of Mental Illness 2015Quantifying the Language of Schizophrenia in Social Media 2017eRISK 2017: CLEF Lab on Early Risk Prediction on the Internet: Experimental Foundations 2015The role of personality, age, and gender in tweeting about mental illness

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