The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Modeling and Understanding Visual Attributes of Mental Health Disclosures in Social Media
Abstract
Content shared on social media platforms has been identified to be valuable in gaining insights into people's mental health experiences. Although there has been widespread adoption of photo-sharing platforms such as Instagram in recent years, the role of visual imagery as a mechanism of self-disclosure is less understood. We study the nature of visual attributes manifested in images relating to mental health disclosures on Instagram. Employing computer vision techniques on a corpus of thousands of posts, we extract and examine three visual attributes: visual features (e.g., color), themes, and emotions in images. Our findings indicate the use of imagery for unique self-disclosure needs, quantitatively and qualitatively distinct from those shared via the textual modality: expressions of emotional distress, calls for help, and explicit display of vulnerability. We discuss the relationship of our findings to literature in visual sociology, in mental health self disclosure, and implications for the design of health interventions.
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“To date, the emergence of machine learning, a powerful computational method involving statistical and mathematical algorithms (Shatte, Hutchinson, & Teague, 2019), has made it possible to study large quantities of data captured from popular social media platforms such as Twitter or Instagram to illuminate various features of mental health (Manikonda & De Choudhury, 2017; Reece et al., 2017).”
From Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice (2020) · cites this paper as Manikonda & De Choudhury, 2017
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Detecting Preventable Disease Risk on Social Media
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Sentiment Analysis of #Meanspo Tweets: Humans vs. Automatic Classifications
Nova, F. F., Shinde, A. S., Pfafman, R., Harrison, A., Delaney, C. L., & Pater, J. A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3701207
Saladié, N., Llorente, C., Joosten, R., & Revuelta, G. (2025) · doi:10.2196/preprints.81230
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Liu, J. “., Su, Y., & Seth, P. (2025) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1267
Comparing Images of Depression in Mass Media and AI-Generated Pictures: Mixed Methods Study
Saladié, N., Llorente, C., Joosten, R., & Revuelta, G. (2026) · JMIR Human Factors · doi:10.2196/81230
Moignan, E. L., Mahoney, J., & Lawson, S. W. (2026) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/6654869
Kapizov, D., & Kumar, P. (2026) · Scientific Journal of Astana IT University · doi:10.37943/xsaj1029
Metadata
- Authors
- Manikonda, L., & De Choudhury, M.
- Affiliations
- Arizona State University (US)
- Georgia Tech, Atlanta, GA, USA
- ORCID
- Manikonda, Lydia
- Venue
- Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2017-05-02
- Pages
- 170-181
- OpenAlex
- W2611167769
- Cited by
- 116
- References
- 54
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 7 · 2023: 14 · 2022: 13 · 2021: 11 · 2020: 27 · 2019: 14 · 2018: 10 · 2017: 4
- FWCI
- 24.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- Office of Naval Research (N00014-13-1-0176,N00014-15-1-2027)
- National Institutes of Health (1R01GM11269701)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- Instagram, visual attributes, image color, emotional distress, vulnerability, visual sociology, social media, health interventions
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