Causal Factors of Effective Psychosocial Outcomes in Online Mental Health Communities
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Abstract
Online mental health communities enable people to seek and provide support, and growing evidence shows the efficacy of community participation to cope with mental health distress. However, what factors of peer support lead to favorable psychosocial outcomes for individuals is less clear. Using a dataset of over 300K posts by ∼39K individuals on an online community TalkLife, we present a study to investigate the effect of several factors, such as adaptability, diversity, immediacy, and the nature of support. Unlike typical causal studies that focus on the effect of each treatment, we focus on the outcome and address the reverse causal question of identifying treatments that may have led to the outcome, drawing on case-control studies in epidemiology. Specifically, we define the outcome as an aggregate of affective, behavioral, and cognitive psychosocial change and identify Case (most improved) and Control (least improved) cohorts of individuals. Considering responses from peers as treatments, we evaluate the differences in the responses received by Case and Control, per matched clusters of similar individuals. We find that effective support includes complex language factors such as diversity, adaptability, and style, but simple indicators such as quantity and immediacy are not causally relevant. Our work bears methodological and design implications for online mental health platforms, and has the potential to guide suggestive interventions for peer supporters on these platforms.
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LIWC is a validated psycholinguistic lexicon and has been widely used in social media data and misinformation empirical and detection work.
“LIWC is a validated psycholinguistic lexicon [104] and has been widely used in social media data and misinformation empirical and detection work [51,91].”
From Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023) · cites this paper as [91]
In “Language Indicative of Mental Health Symptomatic Outcomes” (page unavailable), the paper uses LIWC to analyze language in an online mental health community, supporting its use in social-media research. It does not address misinformation research or detection, or establish that LIWC is widely used in those areas. The separate validation statement is attached to [104], not this source. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe support expression classifiers had k-fold cross-validation accuracies of 0.71 and 0.77 in emotional and informational support classifications, respectively.
“When the predictions of these classifiers were cross-validated with expert annotations from Sharma and De Choudhury’s [38] data, the classifiers were found to have k-fold cross-validation accuracies of 0.71 and 0.77 in ES and IS classifications, respectively [39].”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [39]
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- Authors
- Saha, K., & Sharma, A.
- Affiliations
- Georgia Institute of Technology (US)
- Microsoft Research (India) (IN)
- Corresponding author
- Saha, Koustuv
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2020-05-26
- Volume
- 14
- Pages
- 590-601
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W3026768119
- Cited by
- 72
- References
- 65
- Citations by year
- 2026: 18 · 2025: 11 · 2024: 11 · 2023: 12 · 2022: 6 · 2021: 7 · 2020: 7
- FWCI
- 11.28
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- online mental health communities, peer support, psychosocial outcomes, causal inference, reverse causality, case-control study, peer diversity, language style, online community participation, psychiatric distress
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