The Language of Social Support in Social Media and Its Effect on Suicidal Ideation Risk
Open accessAbstract
Online social support is known to play a significant role in mental well-being. However, current research is limited in its ability to quantify this link. Challenges exist due to the paucity of longitudinal, pre- and post mental illness risk data, and reliable methods that can examine causality between past availability of support and future risk. In this paper, we propose a method to measure how the language of comments in Reddit mental health communities influences risk to suicidal ideation in the future. Incorporating human assessments in a stratified propensity score analysis based framework, we identify comparable subpopulations of individuals and measure the effect of online social support language. We interpret these linguistic cues with an established theoretical model of social support, and find that esteem and network support play a more prominent role in reducing forthcoming risk. We discuss the implications of our work for designing tools that can improve support provisions in online communities.
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The study compared school-specific temporal trends with a control time series from non-school Twitter data to attribute changes to drills.
“The fourth method involved comparing our temporal trends (treatment), speci fi c to the schools and their respective drill dates, with a suitably chosen control time series in the same timeframe (De Choudhury and Kiciman, 2017).”
From Impacts of school shooter drills on the psychological well-being of American K-12 school communities: a social media study (2021) · cites this paper as De Choudhury and Kiciman, 2017
The Data and Method passages describe Reddit users in mental-health communities, not school-specific Twitter data or drill dates. The Method defines treatment as receiving a comment token and control as not receiving it; it does not compare temporal trends against a non-school time series. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredIn the context of suicide, certain types of social support in Reddit communities may reduce the chances of future suicidal ideation among those seeking mental health help.
“In the context of suicide, certain types of social support in Reddit communities may reduce the chances of future suicidal ideation among those seeking mental health help [61].”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [61]
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Li, H. Z., Kraut, R. E., & Zhu, H. (2021) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmab016 · Cited by 29 · see citing works
Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect
Yuan, Y., Saha, K., Keller, B., Isometsä, E. T., & Aledavood, T. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583350 · Cited by 29 · see citing works
Measuring the Impact of Anxiety on Online Social Interactions
Dutta, S., Ma, J. S., & Choudhury, M. D. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.15081 · Cited by 30 · see citing works
All That’s Happening behind the Scenes: Putting the Spotlight on Volunteer Moderator Labor in Reddit
Li, H., Hecht, B. J., & Chancellor, S. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19317 · Cited by 30 · see citing works
Understanding the Online Social Support Dynamics for Postpartum Depression
Progga, F. T., Kumar, A. S., & Rubya, S. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581311 · Cited by 28 · see citing works
Choudhury, M. D., & Kıcıman, E. (2018) · The AI Magazine · doi:10.1609/aimag.v39i3.2815 · Cited by 29 · see citing works
Gong, C., Saha, K., & Chancellor, S. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476039 · Cited by 28 · see citing works
Automatic depression score estimation with word embedding models
Pérez, A., Parapar, J., & Barreiro, Á. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102380 · Cited by 28 · see citing works
Morini, V., Sansoni, M., Rossetti, G., Pedreschi, D., & Castillo, C. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108544 · Cited by 31 · see citing works
Writer Profiling Without the Writer’s Text
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The reflection of offline activities on users’ online social behavior: An observational study
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Machine learning of language use on Twitter reveals weak and non-specific predictions
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Veteran Critical Theory as a Lens to Understand Veterans' Needs and Support on Social Media
Zhou, J., Saha, K., Carron, I. M. L., Yoo, D. W., Deeter, C. R., Choudhury, M. D., & Arriaga, R. I. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512980 · Cited by 26 · see citing works
Swain, V. D., Zhong, Q. J., Parekh, J. R., Jeon, Y., Zimmermann, R., Czerwinski, M. P., Suh, J., Mishra, V., Saha, K., & Hernandez, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713705 · Cited by 33 · see citing works
Supporters First: Understanding Online Social Support on Mental Health from a Supporter Perspective
Kim, M., Saha, K., Choudhury, M. D., & Choi, D. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579525 · Cited by 25 · see citing works
Sequeira, R., Gayen, A., Ganguly, N., Dandapat, S. K., & Chandra, J. (2019) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2019.2943238 · Cited by 22 · see citing works
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Health Misinformation in Search and Social Media
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Text-Based Detection and Understanding of Changes in Mental Health
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Quantifying the Causal Effects of Conversational Tendencies
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Using the Lenses of Emotion and Support to Understand Unemployment Discourse on Reddit
Garg, R., Kapadia, Y., & Sengupta, S. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449088 · Cited by 21 · see citing works
Chancellor, S., Sumner, S. A., David-Ferdon, C. F., Ahmad, T., & Choudhury, M. D. (2021) · JMIR Mental Health · doi:10.2196/24471 · Cited by 23 · see citing works
Matching for Peer Support: Exploring Algorithmic Matching for Online Mental Health Communities
Fang, A., & Zhu, H. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555202 · Cited by 21 · see citing works
Online Self-Disclosure, Social Support, and User Engagement During the COVID-19 Pandemic
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Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders
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Weld, G., West, P., Glenski, M., Arbour, D. T., Rossi, R. A., & Althoff, T. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19362 · Cited by 20 · see citing works
A Social Media Study on Mental Health Status Transitions Surrounding Psychiatric Hospitalizations
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Factors Associated With Increased Dissemination of Positive Mental Health Messaging On Social Media
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Detecting Symptoms of Depression on Reddit
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"I'm gonna KMS": From Imminent Risk to Youth Joking about Suicide and Self-Harm via Social Media
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Detecting Suicide Ideation in the Era of Social Media: The Population Neuroscience Perspective
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Social Media Reveals Psychosocial Effects of the COVID-19 PandemicPreprint
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Learning to Navigate Health Taboos through Online Safe Spaces
Tam, H., Bhat, K., Mohindra, P., & Kumar, N. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580708 · Cited by 12 · see citing works
- Authors
- De Choudhury, M., & Kiciman, E.
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2017-05-03
- Volume
- 11
- Issue
- 1
- Pages
- 32-41
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- PMC
- PMC5565730
- OpenAlex
- W2596823239
- Cited by
- 202
- References
- 42
- Citations by year
- 2026: 15 · 2025: 17 · 2024: 21 · 2023: 25 · 2022: 24 · 2021: 34 · 2020: 27 · 2019: 19 · 2018: 14 · 2017: 6
- FWCI
- 29.26
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM112697)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Clinical Psychology › Suicide and Self-Harm Studies
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Keywords
- online social support, suicidal ideation, social media comments, linguistic analysis, mental illness risk, esteem, network support, online community design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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