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Generalizability of Machine Learning to Categorize Various Mental Illness Using Social Media Activity Patterns

Author

Listed:
  • Chee Siang Ang

    (School of Computing, University of Kent, Canterbury CT2 7NB, UK)

  • Ranjith Venkatachala

    (School of Computing, University of Kent, Canterbury CT2 7NB, UK)

Abstract

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 training datasets did not include keywords or when unrelated datasets were utilized for testing.

Suggested Citation

  • Chee Siang Ang & Ranjith Venkatachala, 2023. "Generalizability of Machine Learning to Categorize Various Mental Illness Using Social Media Activity Patterns," Societies, MDPI, vol. 13(5), pages 1-19, May.
    • Handle: RePEc:gam:jsoctx:v:13:y:2023:i:5:p:117-:d:1140076
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    References listed on IDEAS

    as
    1. Brenda Curtis & Salvatore Giorgi & Anneke E K Buffone & Lyle H Ungar & Robert D Ashford & Jessie Hemmons & Dan Summers & Casey Hamilton & H Andrew Schwartz, 2018. "Can Twitter be used to predict county excessive alcohol consumption rates?," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
    2. Cristina Crocamo & Marco Viviani & Francesco Bartoli & Giuseppe Carrà & Gabriella Pasi, 2020. "Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter’s Users: An Exploratory Content- and Topology-Based Analysis," IJERPH, MDPI, vol. 17(5), pages 1-20, February.
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