Characterizing Audience Engagement and Assessing Its Impact on Social Media Disclosures of Mental Illnesses
Open accessAbstract
Self-disclosures of mental illnesses have been identified to yield coping and therapeutic benefits. An important construct in the self-disclosure process is the audience with whom the individual interacts and shares their experiences. Mental illness self-disclosures are increasingly happening online. However, unlike online support communities where the audience comprises sympathetic peers with similar experiences, what the discloser gains from an ‘invisible’ audience on a general purpose, public social media platform is less understood. Focusing on a highly stigmatized mental illness, schizophrenia, this paper provides the first investigation characterizing the audience of disclosures of this condition on Twitter and how the audience’s engagement impacts future disclosures. Our results are based on a rich year-long temporal analysis of the data of nearly 400 disclosers and their nearly 400 thousand audiences. First, characterizing and modeling the audience engagement temporally, we find evidence of reciprocity in the disclosure process between the discloser and their audience. Then, situating our work in the Social Penetration Theory and operationalizing the disclosure process via a measure of intimacy, an auto-regressive time series model indicates that the patterns of audience engagement and content can forecast changes in the intimacy of disclosures. We discuss the implications for building socially engaging, supportive online spaces for stigmatized mental illness disclosures.
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Twitter is one of the most popular social media platforms, and its public-facing, microblogging-based design enables candid self-disclosure and self-expressions for individuals.
“Twitter is one of the most popular social media platforms, and its public-facing, microblogging–based design enables candid self-disclosure and self-expressions for individuals [27].”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [27]
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Saha, K. (2020) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3406865.3418367
“I will borrow from prior work on operationalizing intimacy of self-disclosure in terms of breadth and depth of disclosure [8].”
KOMUNIKASI INTERPERSONAL DALAM “FASE BULAN MADU” PASANGAN USIA MUDA
Anandita, N., Ramadhani, R. W., & Isa, J. R. S. P. K. M. (2023) · Jurnal Broadcasting Communication · doi:10.53856/bcomm.v5i1.252
Automatic analysis of X (Twitter) data for supporting depression diagnosis
Królak, A., Wiktorski, T., & Żmudzińska, A. (2023) · Human Technology · doi:10.14254/1795-6889.2023.19-3.4
“This makes social media platforms valuable resources to collect data about the society, including the state of publics’ mental health (Mitchell, Hollingshead, & Coppersmith, 2015; Preoţiuc-Pietro et al., 2015; Conway, & O’Connor, 2016; Ernala et al., 2018; Mazuz, & Yom-Tov, 2020).”
BackgroundObserver Effect in Social Media UsePreprint
Saha, K., Gupta, P., Mark, G. J., Kıcıman, E., & Choudhury, M. D. (2024) · Research Square · doi:10.21203/rs.3.rs-2492994/v3
Sharmin, S., Ali, M. S., Jahan, N., Pathan, M. R. K., Rahman, A., Farid, F. A., Karim, H. B. A., & Miah, A. S. M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01728-9
“A linear discriminant analysis model can generate the subjects in engagement content on social media discovered by [24] in order to investigate the engagement implications for people with mental illnesses using unsupervised models.”
Wang, C.-H. A., Pintor, M., Su, N. M., & Chung, C.-F. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3822403
“HCI researchers have examined how users navigate sensitive disclosures [1, 2], engage with audience support [8, 19], and share recovery narratives such as those around anorexia [13].”
- Authors
- Ernala, S. K., Labetoulle, T., Bane, F., Birnbaum, M. L., Rizvi, A. F., Kane, J. M., & De Choudhury, M.
- Affiliations
- Georgia Institute of Technology (US)
- Zucker Hillside Hospital (US)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2018-06-15
- Volume
- 12
- Issue
- 1
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2810163710
- Cited by
- 50
- References
- 44
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 4 · 2023: 7 · 2022: 6 · 2021: 11 · 2020: 10 · 2019: 4 · 2018: 1
- FWCI
- 11.65
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM112697)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Keywords
- schizophrenia, audience engagement, social media, Twitter, intimacy, social penetration theory, online stigma
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