Cited by 116Mental Health via WritingImpact of Technology on AdolescentsInnovative Human-Technology Interaction

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

Manikonda, Lydia, De Choudhury, Munmun·2017·Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

DOI 10.1145/3025453.3025932

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1145/3025453.3025932

Cites

55 of 55 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
Stigma: Notes on the Management of Spoiled Identity
(1969) · Postgraduate Medical Journal · doi:10.1136/pgmj.45.527.642 · Cited by 14,255
Talking about pictures: A case for photo elicitation
Harper, D. (2002) · Visual Studies · doi:10.1080/14725860220137345 · Cited by 3,955
Graph-Based Visual Saliency
Harel, J., Koch, C., & Perona, P. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0073 · Cited by 3,517
Untitled work
(1973) · Medical Entomology and Zoology · Cited by 2,836
The functions of deliberate self-injury: a review of the evidence
Klonsky, E. D. (2007) · Clinical Psychology Review · doi:10.1016/j.cpr.2006.08.002 · Cited by 1,979
Similarity of color images
Stricker, M. A., & Orengo, M. (1995) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.205308 · Cited by 1,749
Predicting Depression via Social Media
Choudhury, M. D., Gamon, M., Counts, S., & Horvitz, E. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14432 · Cited by 1,583

Show all 54 references

Research works printed but not linked: 3

Maeve Duggan , Nicole B Ellison , Cliff Lampe , Amanda Lenhart , and Mary Madden . 2015. Social media update 2014 . Pew Research Center 19 (2015). Maeve Duggan, Nicole B Ellison, Cliff Lampe, Amanda Lenhart, and Mary Madden. 2015. Social media update 2014. Pew Research Center 19 (2015).
10.5555/1211649 · DOI
James W Pennebaker and Cindy K Chung . 2007. Expressive writing, emotional upheavals, and health. Foundations of health psychology ( 2007 ), 263--284. James W Pennebaker and Cindy K Chung. 2007. Expressive writing, emotional upheavals, and health. Foundations of health psychology (2007), 263--284.

Sorted by citations

Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice
Naslund, J. A., Bondre, A., Torous, J., & Aschbrenner, K. A. (2020) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-020-00134-x · Cited by 746
“…statistical and mathematical algorithms (Shatte et al. 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 and De Choudhury 2017; Reece et al. 2017).”
Background
Depression and Self-Harm Risk Assessment in Online Forums
Yates, A., Cohan, A., & Goharian, N. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1322 · Cited by 390
Responsible AI—two frameworks for ethical design practice
Peters, D., Vold, K., Robinson, D., & Calvo, R. A. (2020) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2020.2974991 · Cited by 293
“Technology-speciﬁc guidelines have also been developed, including the guidelines for the design of interventions for mental health on social media [70].”
Background
Beyond Screen Time: Identity Development in the Digital Age
Granic, I., Morita, H., & Scholten, H. (2020) · Psychological Inquiry · doi:10.1080/1047840x.2020.1820214 · Cited by 237
“Facebook, Instagram, and Twitter can also host meaningful discourses around career or education choices, political views, gender identity concerns, and mental health advice (e.g. Manikonda & De Choudhury, 2017).”
Background
HCI and Affective Health
Sanches, P., Janson, A., Karpashevich, P., Nadal, C., Qu, C., Roquet, C. D., Umair, M., Windlin, C., Doherty, G. J., Höök, K., et al. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300475 · Cited by 248
A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media
Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M. B., & De Choudhury, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287587 · Cited by 214
Pocket Skills: A Conversational Mobile Web App To Support Dialectical Behavioral Therapy
Schroeder, J., Wilkes, C., Rowan, K., Toledo, A., Paradiso, A., Czerwinski, M., Mark, G., & Linehan, M. M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173972 · Cited by 175
A survey on big data-driven digital phenotyping of mental health
Liang, Y., Zheng, X., & Zeng, D. D. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.04.001 · Cited by 164
Multimodal mental health analysis in social media
Yazdavar, A. H., Mahdavinejad, M. S., Bajaj, G., Romine, W., Sheth, A., Monadjemi, A. H., Thirunarayan, K., Meddar, J. M., Myers, A., Pathak, J., et al. (2020) · PLOS ONE · doi:10.1371/journal.pone.0226248 · Cited by 133
“Suddenly, we got to become therapists for each other”
O'Leary, K., Schueller, S. M., Wobbrock, J. O., & Pratt, W. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173905 · Cited by 128
The Human in Emotion Recognition on Social Media: Attitudes, Outcomes, Risks
Andalibi, N., & Buss, J. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376680 · Cited by 137
Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review
Gooding, P., & Kariotis, T. (2021) · JMIR Mental Health · doi:10.2196/24668 · Cited by 100
Linguistic Markers Indicating Therapeutic Outcomes of Social Media Disclosures of Schizophrenia
Ernala, S. K., Rizvi, A. F., Birnbaum, M. L., Kane, J. M., & De Choudhury, M. (2017) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3134678 · Cited by 99
Discovering Alternative Treatments for Opioid Use Recovery Using Social Media
Chancellor, S., Nitzburg, G. C., Hu, A., Zampieri, F., & Choudhury, M. D. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300354 · Cited by 91
What Twitter Profile and Posted Images Reveal about Depression and Anxiety
Guntuku, S. C., Preoțiuc-Pietro, D., Eichstaedt, J. C., & Ungar, L. (2019) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v13i01.3225 · Cited by 87
Beyond self-reflection: introducing the concept of rumination in personal informatics
Eikey, E. V., Caldeira, C. M., Figueiredo, M. C., Chen, Y., Borelli, J. L., Mazmanian, M., & Zheng, K. (2021) · Personal and Ubiquitous Computing · doi:10.1007/s00779-021-01573-w · Cited by 82
The Potential of Game-Based Digital Biomarkers for Modeling Mental Health
Mandryk, R. L., & Birk, M. V. (2019) · JMIR Mental Health · doi:10.2196/13485 · Cited by 93
Trans Time
Haimson, O. L., Buss, J., Weinger, Z., Starks, D. L., Gorrell, D., & Baron, B. S. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415195 · Cited by 83
Beyond the Coded Gaze
Feuston, J. L., & Piper, A. M. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274320 · Cited by 72
Everyday Experiences
Feuston, J. L., & Piper, A. M. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300495 · Cited by 63
Disability and the COVID-19 Pandemic: Using Twitter to Understand Accessibility during Rapid Societal Transition
Gleason, C., Valencia, S., Kirabo, L., Wu, J., Guo, A., Jeanne Carter, E., Bigham, J., Bennett, C., & Pavel, A. (2020) · Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3373625.3417023 · Cited by 53
Understanding Moderation in Online Mental Health Communities
Saha, K., Ernala, S. K., Dutta, S., Sharma, E., & Choudhury, M. D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49576-3_7 · Cited by 49
Geollery
Du, R., Li, D., & Varshney, A. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300915 · Cited by 53
Exploring the Effect of Social Support and Empathy on User Engagement in Online Mental Health Communities
Chen, Y., & Yang, X. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18136855 · Cited by 56
Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone Sensing
Wang, W., Mirjafari, S., Harari, G. M., Ben‐Zeev, D., Brian, R., Choudhury, T., Hauser, M., Kane, J. M., Masaba, K., Nepal, S. K., et al. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376855 · Cited by 47
Data Subjects' Conceptualizations of and Attitudes Toward Automatic Emotion Recognition-Enabled Wellbeing Interventions on Social Media
Roemmich, K., & Andalibi, N. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476049 · Cited by 54
Attitudes and Folk Theories of Data Subjects on Transparency and Accuracy in Emotion Recognition
Grill, G., & Andalibi, N. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512925 · Cited by 49
"It's hard to argue with a computer"
Hirsch, T., Soma, C. S., Merced, K., Kuo, P. B., Dembe, A., Caperton, D. D., Atkins, D. C., & Imel, Z. E. (2018) · Designing Interactive Systems Conference · doi:10.1145/3196709.3196776 · Cited by 42
“I think people are powerful”: The Sociality of Individuals Managing Depression
Burgess, E. R., Ringland, K. E., Nicholas, J., Knapp, A. A., Eschler, J., Mohr, D., & Reddy, M. C. (2019) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3359143 · Cited by 60
Exploring and Designing for Memory Impairments in Depression
Qu, C., Sas, C., & Doherty, G. J. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300740 · Cited by 41
Extracting depressive symptoms and their associations from an online depression community
Yao, X., Yu, G., Tang, J., & Zhang, J. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106734 · Cited by 39
Image Clustering: An Unsupervised Approach to Categorize Visual Data in Social Science Research
Zhang, H., & Peng, Y. (2022) · Sociological Methods & Research · doi:10.1177/00491241221082603 · Cited by 44
“Help Me:” Examining Youth’s Private Pleas for Support and the Responses Received from Peers via Instagram Direct Messages
Huh, J., Razi, A., Nguyen, D. N., Regmi, S., & Wiśniewski, P. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581233 · Cited by 42
The Influence of Instagram Influencers’ Activity on Green Consumption Behavior
Jalali, S. S., & Khalid, H. B. (2021) · Business Management and Strategy · doi:10.5296/bms.v12i1.18265 · Cited by 34
Detecting Reddit Users with Depression Using a Hybrid Neural Network SBERT-CNN
Chen, Z., Yang, R., Fu, S., Zong, N., Liu, H., & Huang, M. S. (2023) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi57859.2023.00035 · Cited by 44
Psychological Determinants of Consumer’s Usage, Satisfaction, and Word-of-Mouth Recommendations Toward Smart Voice Assistants
Mishra, A. A., & Shukla, A. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-64849-7_24 · Cited by 31
Health Monitoring on Social Media over Time
Sidana, S., Amer-Yahia, S., Clausel, M., Rebai, M., Mai, S. T., & Amini, M.-R. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2795606 · Cited by 29
Integrating Artificial and Human Intelligence in Complex, Sensitive Problem Domains: Experiences from Mental Health
Choudhury, M. D., & Kıcıman, E. (2018) · The AI Magazine · doi:10.1609/aimag.v39i3.2815 · Cited by 29
Effects of Support-Seekers’ Community Knowledge on Their Expressed Satisfaction with the Received Comments in Mental Health Communities
Peng, Z., Ma, X., Yang, D., Tsang, K. W., & Guo, Q. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445446 · Cited by 26
Data-Driven Implications for Translating Evidence-Based Psychotherapies into Technology-Delivered Interventions
Schroeder, J., Suh, J., Wilks, C. R., Czerwinski, M. P., Munson, S. A., Fogarty, J. A., & Althoff, T. (2020) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3421937.3421975 · Cited by 27
"Do You Ladies Relate?": Experiences of Gender Diverse People in Online Eating Disorder Communities
Feuston, J. L., DeVito, M. A., Scheuerman, M. K., Weathington, K., Benitez, M., Perez, B. Z., Sondheim, L., & Brubaker, J. R. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555145 · Cited by 25
Designing a Reclamation of Body and Health
Eschler, J., Bhattacharya, A., & Pratt, W. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174084 · Cited by 26
Shaping the Design of Smartphone-Based Interventions for Self-Harm
Honary, M., Bell, B. T., Clinch, S., Vega, J., Kroll, L., Sefi, A., & McNaney, R. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376370 · Cited by 21
Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders
Hänsel, K., Lin, I. W., Sobolev, M., Muscat, W., Yum-Chan, S., Choudhury, M. D., Kane, J. M., & Birnbaum, M. L. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.691327 · Cited by 18
What users’ musical preference on Twitter reveals about psychological disorders
Alavijeh, S. Z., Zarrinkalam, F., Noorian, Z., Mehrpour, A., & Etminani, K. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103269 · Cited by 23
A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach
Ahmed, M. S., & Ahmed, N. (2023) · JMIR Formative Research · doi:10.2196/28848 · Cited by 22
#T1DLooksLikeMe: Exploring Self-Disclosure, Social Support, and Type 1 Diabetes on Instagram
Holtz, B., & Kanthawala, S. (2020) · Frontiers in Communication · doi:10.3389/fcomm.2020.510278 · Cited by 19
Patient-Generated Health Photos and Videos Across Health and Well-being Contexts: Scoping Review
Ploderer, B., Rezaei Aghdam, A., & Burns, K. (2022) · Journal of Medical Internet Research · doi:10.2196/28867 · Cited by 18
Enhancing relationships through technology: directions in parenting, caregiving, romantic partnerships, and clinical practice
Morris, M. E. (2020) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2020.22.2/mmorris · Cited by 23
Social Support is Contagious: Exploring the Effect of Social Support in Online Mental Health Communities
Chen, Y., & Yang, X. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451644 · Cited by 18
Mental health memes: beneficial or aversive in relation to psychiatric symptoms?
Akram, U., & Drabble, J. (2022) · Humanities and Social Sciences Communications · doi:10.1057/s41599-022-01381-4 · Cited by 20
Beyond Mobile Apps: A Survey of Technologies for Mental Well-Being
Woodward, K., Kanjo, E., Brown, D. J., McGinnity, T. M., Inkster, B., Macintyre, D. J., & Tsanas, A. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3015018 · Cited by 18
The Authority of "Fair" in Machine LearningPreprint
Skirpan, M. W., & Gorelick, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.09976 · Cited by 28
The Reintegration Journey Following a Psychiatric Hospitalization: Examining the Role of Social Technologies
Ernala, S. K., Seybolt, J., Yoo, D. W., Birnbaum, M. L., Kane, J. M., & Choudhury, M. D. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512969 · Cited by 22
"Money Doesn't Buy You Happiness": Negative Consequences of Using the Freemium Model for Mental Health Apps
Eagle, T., Mehrotra, A., Sharma, A., Zuniga, A., & Whittaker, S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555155 · Cited by 16
BlueMemo: Depression Analysis through Twitter Posts
Hu, P., Lin, C., Su, H., Li, S., Han, X., Zhang, Y., & Mei, J. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/760 · Cited by 16
"I'm gonna KMS": From Imminent Risk to Youth Joking about Suicide and Self-Harm via Social Media
Ali, N. S., Qadir, S., Alsoubai, A., Choudhury, M. D., Razi, A., & Wiśniewski, P. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642489 · Cited by 13
The Subtleties of Self-Presentation: A study of sensitive disclosure among sexual minority adolescents
Pinch, A. C., Birnholtz, J. P., Macapagal, K. R., Kraus, A., & Moskowitz, D. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637408 · Cited by 12
"Unrest and trauma stays with you!": Navigating mental health and professional service-seeking in Kashmir
Wani, A. S., Joshi, I., Nahvi, N. I., & Singh, P. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642507 · Cited by 15
MT-GPD: A Multimodal Deep Transfer Learning Model Enhanced by Auxiliary Mechanisms for Cross-Domain Online Fake News Detection
Zhang, D., Shan, G., Lee, M., Zhou, L., & Fu, Z. (2025) · Production and Operations Management · doi:10.1177/10591478251319686 · Cited by 13
Information-seeking vs. sharing: Which explains regional health? An analysis of Google Search and Twitter trends
Jaidka, K., Eichstaedt, J. C., Giorgi, S., Schwartz, H. A., & Ungar, L. (2020) · Telematics and Informatics · doi:10.1016/j.tele.2020.101540 · Cited by 16
A Study on Mental Health Discussion through Reddit
Kamarudin, N. S., Beigi, G., & Liu, H. (2021) · International Conference on Software Engineering and Computer Systems · doi:10.1109/icsecs52883.2021.00122 · Cited by 10
From methods to datasets: a detailed study on facial emotion recognition
Nidhi, & Verma, B. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05052-y · Cited by 13
Images Connect Us Together: Navigating a COVID-19 Local Outbreak in China Through Social Media Images
He, C., He, L., Yang, W., & Li, B. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637349 · Cited by 9
Understanding how social media imagery empowers caregivers: an analysis of microcephaly in Latin America
Motti, V. G., Kalantari, N., & Néris, V. P. D. A. (2020) · Personal and Ubiquitous Computing · doi:10.1007/s00779-020-01418-y · Cited by 8
Classification of Mental Illnesses from Reddit Posts Using Sentence-BERT Embeddings and Neural Networks
Wagay, F. A., & Jahiruddin (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.398 · Cited by 7
Towards the development of ethics guidelines for visual psychology: A review of relevant visual research ethics guidelines
Hansen, S., & Colucci, E. (2020) · QMiP Bulletin · doi:10.53841/bpsqmip.2020.1.30.83 · Cited by 6
Towards Identifying Fine-Grained Depression Symptoms from Memes
Yadav, S., Caragea, C., Zhao, C., Kumari, N. P., Solberg, M. A., & Sharma, T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.495 · Cited by 8
Making the Implicit Explicit: Depression Detection in Web across Posted Texts and Images
Hu, P., Lin, C., Li, J., Tan, F., Han, X., Zhou, X., & Hu, L. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385590 · Cited by 6
Large-Scale Sleep Condition Analysis Using Selfies from Social Media
Peng, X., Luo, J., Glenn, C. R., Zhan, J., & Liu, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-60240-0_19 · Cited by 6
Social Media for Mental Health: Data, Methods, and Findings
Kamarudin, N. S., Beigi, G., Manikonda, L., & Liu, H. (2020) · Lecture notes in social networks · doi:10.1007/978-3-030-41251-7_8 · Cited by 11
Self-citation
An empirical study on prediction of population health through social media
Nguyen, H. S., Nguyen, T., & Nguyen, D. T. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103277 · Cited by 6
MentalImager: Exploring Generative Images for Assisting Support-Seekers' Self-Disclosure in Online Mental Health Communities
Zhang, H., Zhang, J., Zhou, Y., Louie, R., Kim, T., Guo, Q., Li, S., & Peng, Z. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711031 · Cited by 12
Jointly Predicting Affective and Mental Health Scores Using Deep Neural Networks of Visual Cues on the Web
Nguyen, H., Nguyen, V., Nguyen, T., Larsen, M. E., O’Dea, B., Nguyen, D. T., Le, T., Phung, D., Venkatesh, S., & Christensen, H. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02925-8_7 · Cited by 12
Who is worthy of help? Constructing the stereotype of the “ideal victim” of child sexual abuse
Eelmaa, S., & Murumaa-Mengel, M. (2022) · Nomos Verlagsgesellschaft mbH & Co. KG eBooks · doi:10.5771/9783748904403-261 · Cited by 5
Analyzing relationship between user‐generated content and local visual information with augmented reality‐based location‐based social networks
Liu, C., & Fuhrmann, S. (2020) · Transactions in GIS · doi:10.1111/tgis.12630 · Cited by 5
Unfiltered: How Teens Engage in Body Image and Shaming Discussions via Instagram Direct Messages (DMs)
Alluhidan, A., Park, J., Akter, M., Rodgers, R. F., Razi, A., & Wiśniewski, P. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757698 · Cited by 6
A Hybrid Fuzzy System via Topic Model for Recommending Highlight Topics of CQA in Developer Communities
Jelodar, H., Wang, Y., Vajdi, A., Rabbani, M., Zhao, R., Boukela, L., & Li, H. (2020) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126620502485 · Cited by 4
Serapis: a generative AI model to produce social media intelligence for depression diagnosis
Foppa, A. A., Feijó, L. P., & Barbosa, J. L. V. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2633775 · Cited by 3
Stories That Heal: Characterizing and Supporting Narrative for Suicide Bereavement
Doyle, D. T., Ghosh, J. K., Suchocki, R., Keegan, B., Voida, S., & Brubaker, J. R. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31319 · Cited by 3
Can Large Language Models Grasp Abstract Visual Concepts in Videos? A Case Study on YouTube Shorts about Depression
Liu, J., Su, Y., & Seth, P. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719821 · Cited by 3
Anxiety detection using the AMAS-C test and feeling analysis on the Facebook social network
Ávila, D., Altamirano, A., Avila, J., & Guerrero, G. (2020) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.23919/cisti49556.2020.9141104 · Cited by 3
Shaping the Design of Smartphone-Based Interventions for Self-HarmPreprint
Bell, B. T. (2020) · doi:10.31234/osf.io/eu5pw · Cited by 3
The collective emotion of mentally ill individuals within Facebook groups during Covid-19 pandemic
Rothschild, N., Schler, J., Sarne, D., & Aharony, N. (2024) · Aslib Journal of Information Management · doi:10.1108/ajim-08-2023-0320 · Cited by 2
Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features
Halimeh, H., Caron, M., & Müller, O. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.415 · Cited by 2
Predicting Depression with Social Media Images
Shivangi, Sangeeta, S., & Rautela, R. (2024) · IEEE Karachi Section Humanitarian Technology Conference (KHI-HTC) · doi:10.1109/khi-htc60760.2024.10481967 · Cited by 2
Where is the harm? Exploring online peer-support forums for people sexually attracted to minors as platforms to prevent child sexual abuse
Pedersen, M. R. (2022) · Nomos Verlagsgesellschaft mbH & Co. KG eBooks · doi:10.5771/9783748904403-279 · Cited by 2
Self-Tracking Emotional States through Social Media Mobile Photography
Nave, C., Romão, T., & Correia, N. M. R. (2018) · Electronic workshops in computing · doi:10.14236/ewic/hci2018.146 · Cited by 2
Fusing Visual, Textual and Connectivity Clues for Studying Mental HealthPreprint
Yazdavar, A. H., Mahdavinejad, M. S., Bajaj, G., Romine, W. L., Monadjemi, A., Thirunarayan, K., Sheth, A., & Pathak, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06843 · Cited by 2
Um Modelo Computacional para Análise de Depressão em Dados de Redes Sociais
Foppa, A. A., & Barbosa, J. L. V. (2025) · doi:10.5753/sbcas.2025.6939 · Cited by 1
With Visual Integrity and Care: A Framework for Mixed Methods Research on Visual Social Data
Lutz, N., Schafer, J. S., Dhawka, P., Tinn, P., & Starbird, K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791623 · Cited by 2
Unveiling trauma discourse: narratives, behavior, and support in online communities
Appling, S., Shani, C., Sherrill, A. M., & Arriaga, R. I. (2026) · Academia Mental Health and Well-Being · doi:10.20935/mhealthwellb8354 · Cited by 1
Harnessing the Power of Data Science to Grasp Insights About Human Behaviour, Thinking, and Feeling from Social Media Images
Dudău, D. P. (2021) · Computational social sciences · doi:10.1007/978-3-030-54936-7_5 · Cited by 1
Health Talk: Understanding Practices of Popular Professional YouTubers
Phan, T., Michoud, C., Volpato, L., Carral, M. D. R., & Gática-Pérez, D. (2022) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3568444.3568453 · Cited by 1
Predicting Community Health Through Heterogeneous Social Networks
Nguyen, H., & Le, H. (2023) · SN Computer Science · doi:10.1007/s42979-023-01718-z · Cited by 1
SAD CULTURE: AN ANALYSIS OF THE US’ MENTAL HEALTH ISSUES AS EXPRESSED IN SOCIAL MEDIA
Fajri, B. A., Windarti, A., Ahdiani, U., & Atmantika, Z. H. (2023) · Rubikon Journal of Transnational American Studies · doi:10.22146/rubikon.v10i2.83832 · Cited by 1
Understanding Emotional Disclosure via Diary-keeping in Quarantine on Social Media
Deng, Y., He, C., & Li, B. (2023) · International Symposium of Chinese CHI (Chinese CHI) · doi:10.1145/3629606.3629623 · Cited by 3
Exploring Users’ Text and Image Posting Behaviors in Weibo Mental Health Communities
Lin, X., Cheng, J., Wang, S., Song, K., Wang, J., & Peng, Z. (2022) · International Symposium of Chinese CHI (Chinese CHI) · doi:10.1145/3565698.3565797 · Cited by 1
Understanding how Social Media Imagery EmpowersCaregivers: an Analysis of Microcephaly in Latin America
Motti, V. G., & Kalantari, N. (2019) · doi:10.4108/eai.20-5-2019.2283541 · Cited by 1
A Study of User Behaviors and Activities on Online Mental Health Communities
Kamarudin, N. S. (2019) · Cited by 1

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
DOI: 10.1145/3025453.3025932
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
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
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 ›

Are you an author, or do you see a problem with this page? Request a review

Modeling and Understanding Visual Attributes of Mental Health Disclosures in Social Media

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks