ArticleNpj mental health research2024
Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study.
Article in Npj mental health research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Exploiting topic analysis models to explore psychological dimensions in social media data.Scientific reports · 2026Article
- A study on the coupling mechanism between the urban environment and depression perception based on deep learning and street view image.Scientific reports · 2026Article
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Authors and funding
3 authors.
Funding
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Abstract
Depression affects over 280 million people globally, yet many cases remain undiagnosed or untreated due to stigma and lack of awareness. Social media platforms like X (formerly Twitter) offer a way to monitor and analyze depression markers. This study analyzes Twitter data 90 days before and 90 days after a self-disclosed clinical diagnosis. We gathered 246,637 tweets from 229 diagnosed users. CorEx topic modeling identified seven themes: causes, physical symptoms, mental symptoms, swear words, treatment, coping/support mechanisms, and lifestyle, and conditional logistic regression assessed the odds of these themes occurring post-diagnosis. A control group of healthy users (284,772 tweets) was used to develop and evaluate machine learning classifiers-support vector machines, naive Bayes, and logistic regression-to distinguish between depressed and non-depressed users. Logistic regression and SVM performed best. These findings show the potential of Twitter data for tracking depression and changes in symptoms, coping mechanisms, and treatment use.
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Registered trials
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