Abstract
Previous work has investigated the identification of mental health issues in social media users, yet the way that users’ mental states and related behavior change over time remains relatively understudied. This paper focuses on online mental health communities and studies how users’ contributions to these communities change over one year. We define a metric called the Mental Health Contribution Index (MHCI), which we use to measure the degree to which users’ contributions to mental health topics change over a one-year period. In this work, we study the relationship between MHCI scores and the online expression of mental health symptoms by extracting relevant linguistic features from user-generated content and conducting statistical analyses. Additionally, we build a classifier to predict whether or not a user’s contributions to mental health subreddits will increase or decrease. Finally, we employ propensity score matching to identify factors that correlate with an increase or a decrease in mental health forum contributions. Our work provides some of the first insights into detecting and understanding social media users’ changes in mental health states over time.
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Acknowledgement
We thank all anonymous reviewers for their constructive suggestions on our work. We also thank Dr. Márcio Duarte Albasini Mourão for helpful discussions with us on RQ1. This work is partly supported by the Michigan Institute for Data Science, by the National Science Foundation under grant #1344257 and by the John Templeton Foundation under grant #48503.
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Li, Y., Mihalcea, R., Wilson, S.R. (2018). Text-Based Detection and Understanding of Changes in Mental Health. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11186. Springer, Cham. https://doi.org/10.1007/978-3-030-01159-8_17
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DOI: https://doi.org/10.1007/978-3-030-01159-8_17
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