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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Machine learning 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.
“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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- 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
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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