[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120690-en":3,"doc-seo-120690-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120690,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Generalizability of Machine Learning to Categorize Various Mental Illness Using Social Media Activity Patterns","Mental illness is treated as a major global health challenge that harms well-being and productivity. This study evaluates how well machine learning models generalize across social media platforms by using language samples drawn from Reddit and Twitter communities focused on anxiety, autism, schizophrenia, depression, bipolar disorder, and BPD. Models trained on labeled Twitter data (CNN and Word2vec) are tested on Reddit data, and vice versa, using 606,208 posts and 23,102,773 tweets. Results show effective classification even when target datasets lacked specific keywords or were unrelated.","Kent Academic Repository  \nAng, Chee Siang and Venkatachala, Ranjith (2023) Generalizability of Machine Learning to Categorize Various Mental Illness Using Social Media Activity Patterns. Societies, 13 (5). ISSN 2075-4698.  \nDownloaded from  \n[https://kar.kent.ac.uk/101272/](https://kar.kent.ac.uk/101272/ The University of Kent)[ The University of Kent](https://kar.kent.ac.uk/101272/ The University of Kent)'s Academic Repository KAR  \nThe version of record is available from  \n[https://doi.org/10.3390/soc130501](https://doi.org/10.3390/soc130501) 17  \nThis document version  \nPublisher pdf  \nDOI for this version  \nLicence for this version  \nCC BY (Attribution)  \nAdditional information  \nVersions of research works  \nVersions of Record  \nIf this version is the version of record, it is the same as the published version available on the publisher's web site. Cite as the published version.  \nAuthor Accepted Manuscripts  \nIf this document is identified as the Author Accepted Manuscript it is the version after peer review but before typesetting, copy editing or publisher branding. Cite as Surname, Initial. (Year) 'Title of article'. To be published in Title of Journal , Volume and issue numbers [peer-reviewed accepted version] . Available at: DOI or URL (Accessed: date) .  \nEnquiries  \nIf you have questions about this [document contact ](document contact ResearchSupport@kent.ac.uk. Please)[ResearchSupport@kent.ac.uk](document contact ResearchSupport@kent.ac.uk. Please)[. Please](document contact ResearchSupport@kent.ac.uk. Please) include the URL of the record in KAR. If you believe that your, or a third party's rights have been compromised through this document please see our Take Down policy (available from [https://www.kent.ac.uk/guides/kar-the-kent-academic-repository\\#policies](https://www.kent.ac.uk/guides/kar-the-kent-academic-repository#policies)) .  \nsocieties   \nArticle  \nGeneralizability of Machine Learning to Categorize Various Mental Illness Using Social Media Activity Patterns  \nChee Siang Ang and Ranjith Venkatachala *  \nCitation: Ang, C.S.; Venkatachala, R. Generalizability of Machine Learning to Categorize Various Mental Illness Using Social Media Activity Patterns. Societies 2023, 13, 117. [https://](https://)[ ](https://)[doi.org/10.3390/soc13050117](doi.org/10.3390/soc13050117)  \nAcademic Editor: Michael A. Stefanone  \nReceived: 9 February 2023  \nRevised: 10 April 2023  \nAccepted: 26 April 2023  \nPublished: 5 May 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nSchool of Computing, University of Kent, Canterbury CT2 7NB, UK  \n* Correspondence: [rv231@kent.ac.uk](rv231@kent.ac.uk)  \nAbstract: Mental illness has recently become a global health issue, causing significant suffering in people's lives and having a negative impact on productivity. In this study, we analyzed the generalization capacity of machine learning to classify various mental illnesses across multiple social media platforms (Twitter and Reddit) . Language samples were gathered from Reddit and Twitter postings in discussion forums devoted to various forms of mental illness (anxiety, autism, schizophrenia, depression, bipolar disorder, and BPD) . Following this process, information from 606,208 posts (Reddit) created by a total of 248,537 people and from 23,102,773 tweets was used for the analysis. We initially trained and tested machine learning models (CNN and Word2vec) using labeled Twitter datasets, and then we utilized the dataset from Reddit to assess the effectiveness of our trained models and vice versa. According to the experimental findings, the suggested method successfully classified mental illness in social media texts even when tr","cbCailWX8G1kV8GX","https://ap.wps.com/l/cbCailWX8G1kV8GX","pdf",2611081,1,20,"English","en",105,"# Introduction\n## Biopsychosocial perspective on mental health and illness\n## Scope of mental illnesses and societal impact\n# Methods\n## Data collection from Twitter and Reddit\n## Model training and cross-platform evaluation\n# Results\n## Classification performance under keyword absence and unrelated testing\n# Conclusion","[{\"question\":\"What does the study evaluate about machine learning models for mental illness classification?\",\"answer\":\"It evaluates the models’ generalization capacity across multiple social media platforms by testing trained models on data from different platforms.\"},{\"question\":\"Which social media sources and mental illness categories are used?\",\"answer\":\"Language samples come from Reddit and Twitter posts, covering anxiety, autism, schizophrenia, depression, bipolar disorder, and BPD.\"},{\"question\":\"What training and testing strategy is used to assess cross-platform effectiveness?\",\"answer\":\"Models are first trained and tested on labeled Twitter datasets, then evaluated using Reddit data to measure effectiveness, and the reverse direction is also performed.\"}]","Generalizability of Machine Learning to Categorize Various Mental Illness Using Social Media Activity Patterns | 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