Mental Health on Reddit: Explainable Classification with Transformers

Mental Health on Reddit: Explainable Classification with Transformers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mental Health on Reddit: Explainable Classification with Transformers Irene Sánchez Rodríguez, John Bianchi, Fabio Pinelli, Folco Panizza, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6392473/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jan, 2026 Read the published version in Scientific Reports → Version 2 posted 10 You are reading this latest preprint version Show more versions Abstract Social media is a powerful tool also for discussing mental health. The conversations that take place in these spaces provide a unique insight into how users talk about the issue. This study uses state-of-the-art transformer models, BERT and MentalBERT, to classify Reddit posts about anxiety, depression, bipolar disorder and borderline personality disorder (BPD) in specialised subreddits. By assessing how well subreddit conversations align with their intended mental health focus, the analysis ensures that these communities are effectively serving their purpose as support spaces.Our classification models achieves an average accuracy of 82%, with MentalBERT slightly outperforming BERT. To ensure transparency, we use Local Interpretable Model-agnostic Explanations (LIME) to identify key linguistic patterns that influence the model predictions. The outcome reveals distinct language use across conditions: as examples, discussions in bipolar disorder subreddits often refer to mood instability, while BPD communities emphasise challenges in emotional regulation. By integrating classification with explainability, this study provides insights that can help mental health professionals understand trends in online discourses and support platform moderation efforts to promote more effective and supportive digital environments. This, in turn, can help to raise awareness and reduce stigma around mental health issues. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Jan, 2026 Read the published version in Scientific Reports → Version 2 posted Editorial decision: Revision requested 18 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviews received at journal 12 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 09 Aug, 2025 Reviews received at journal 29 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers invited by journal 04 Jul, 2025 Submission checks completed at journal 01 Jul, 2025 First submitted to journal 27 Jun, 2025 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6392473","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2025-04-15 04:09:32","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific 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The conversations that take place in these spaces provide a unique insight into how users talk about the issue. This study uses state-of-the-art transformer models, BERT and MentalBERT, to classify Reddit posts about anxiety, depression, bipolar disorder and borderline personality disorder (BPD) in specialised subreddits. By assessing how well subreddit conversations align with their intended mental health focus, the analysis ensures that these communities are effectively serving their purpose as support spaces.Our classification models achieves an average accuracy of 82%, with MentalBERT slightly outperforming BERT. To ensure transparency, we use Local Interpretable Model-agnostic Explanations (LIME) to identify key linguistic patterns that influence the model predictions. 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