A computational study of mental health awareness campaigns on social media
Open accessAbstract
Social media data like that from Twitter can offer new information about how groups of people perceive their medications, share benefits, and report side effects.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
In a computational study of mental health awareness campaigns on Twitter, stigmatizing content was rare but was the most spread (re-tweeted).
“while stigmatizing content was rare, it was actually the most spread (re-tweeted) demonstrating that harmful content can travel quickly on social media (Saha et al., 2019)”
From Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice (2020) · cites this paper as Saha et al., 2019
Harmful content can travel quickly on social media.
“demonstrating that harmful content can travel quickly on social media (Saha et al., 2019)”
From Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice (2020) · cites this paper as Saha et al., 2019
In a computational study of mental health awareness campaigns on Twitter, inspirational posts and tips were the most shared.
“A computational study of mental health awareness campaigns on Twitter provides further support with inspirational posts and tips being the most shared (Saha et al., 2019).”
From Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice (2020) · cites this paper as Saha et al., 2019
Prior work found that mental health discourse on Twitter ranges across stigmatizing, inspirational, resource, medical, and social dimensions of expressions.
“Prior work found that mental health discourse on Twitter ranges across stigmatizing, inspirational, resource, medical, and social dimensions of expressions [100]”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [100]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Users of the world, unite! The challenges and opportunities of Social Media
Kaplan, A. M., & Haenlein, M. (2010) · Business Horizons · doi:10.1016/j.bushor.2009.09.003 · Cited by 17,875 · cited 1× · see citing works
“It has been well documented that social media constitutes an immensely powerful source of social influence [5], with an ability to help individuals frame opinions on topics they care about, or to alter attitudes and perceptions around events and issues [6].”
BackgroundA Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909 · see citing works
Illusion and well‐being: A social psychological perspective on mental health
Taylor, S. E., & Brown, J. D. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.2.193 · Cited by 7,814 · see citing works
Multidimensional binary search trees used for associative searching
Bentley, J. L. (1975) · Communications of the ACM · doi:10.1145/361002.361007 · Cited by 7,659 · see citing works
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405 · see citing works
The Stanford CoreNLP Natural Language Processing Toolkit
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S. J., & McClosky, D. (2014) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.3115/v1/p14-5010 · Cited by 7,171 · see citing works
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.09091379 · Cited by 7,067 · see citing works
Social media? Get serious! Understanding the functional building blocks of social media
Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011) · Business Horizons · doi:10.1016/j.bushor.2011.01.005 · Cited by 4,970 · see citing works
Semi-Supervised Learning Literature Survey
Zhu, X. (2005) · Cited by 3,828 · see citing works
Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022 · cited 1× · see citing works
“Note that, these two measures have been situated in prior literature as reliable indicators of content engagement on Twitter [31].”
BackgroundAn Algorithm for Finding Best Matches in Logarithmic Expected Time
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1977) · ACM Transactions on Mathematical Software · doi:10.1145/355744.355745 · Cited by 2,802 · see citing works
FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION
(2009) · Proceedings of the Fourth International Conference on Computer Vision Theory and Applications · doi:10.5220/0001787803310340 · Cited by 2,640 · see citing works
Wright, K. B. (2006) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2005.tb00259.x · Cited by 2,550 · see citing works
Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., & Hoving, C. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.1933 · Cited by 2,517 · see citing works
Estimating the true global burden of mental illness
Vigo, D., Thornicroft, G., & Atun, R. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00505-2 · Cited by 2,494 · see citing works
Linguistic Inquiry and Word Count (LIWC)
Chung, C. K., & Pennebaker, J. W. (2012) · IGI Global eBooks · doi:10.4018/978-1-60960-741-8.ch012 · Cited by 2,152 · see citing works
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700 · see citing works
We’re all connected: The power of the social media ecosystem
Hanna, R. C., Rohm, A. J., & Crittenden, V. L. (2011) · Business Horizons · doi:10.1016/j.bushor.2011.01.007 · Cited by 1,659 · see citing works
Eysenbach, G. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1157 · Cited by 1,351 · see citing works
The political power of social media: Technology, the public sphere, and political change
Shirky, C. (2011) · Foreign Affairs · Cited by 1,193 · see citing works
Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures
Golder, S. A., & Macy, M. W. (2011) · Science · doi:10.1126/science.1202775 · Cited by 1,155 · cited 1× · see citing works
“…pronouns in conjunction with interactive verbs (ask, tell, listen, etc.) is typically associated with social processes and social interaction [41], aspects key to raising awareness and dissemination of social media information, and therefore likely to surface in many categories in our…”
BackgroundSemi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]
Chapelle, O., Schölkopf, B., & Zien, E. A. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2009.2015974 · Cited by 1,102 · see citing works
Social media use in the United States: implications for health communication
Chou, W.-Y. S., Hunt, Y. M., Beckjord, E. B., Moser, R. P., & Hesse, B. W. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1249 · Cited by 1,082 · see citing works
Harnessing Social Media for Health Promotion and Behavior Change
Korda, H., & Itani, Z. (2011) · Health Promotion Practice · doi:10.1177/1524839911405850 · Cited by 939 · see citing works
Tufekci, Z. (2014) · International Conference on Web and Social Media · doi:10.48550/arxiv.1403.7400 · Cited by 872 · see citing works
Hawn, C. (2009) · Health Affairs · doi:10.1377/hlthaff.28.2.361 · Cited by 821 · see citing works
Tufekci, Z. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14517 · Cited by 780 · see citing works
Social Media: A Review and Tutorial of Applications in Medicine and Health Care
Grajales III, F. J., Sheps, S., Ho, K., Novak-Lauscher, H., & Eysenbach, G. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2912 · Cited by 737 · see citing works
Quantifying Mental Health Signals in Twitter
Coppersmith, G., Dredze, M., & Harman, C. (2014) · Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality · doi:10.3115/v1/w14-3207 · Cited by 722 · see citing works
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
Choudhury, M. D., Kıcıman, E., Dredze, M. H., Coppersmith, G., & Kumar, M. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858207 · Cited by 682 · see citing works
Guo, C., & Saxton, G. D. (2013) · Nonprofit and Voluntary Sector Quarterly · doi:10.1177/0899764012471585 · Cited by 546 · see citing works
(2007) · Cited by 521 · see citing works
Social media and suicide: A public health perspective
Luxton, D. D., June, J. D., & Fairall, J. M. (2012) · American Journal of Public Health · doi:10.2105/ajph.2011.300608 · Cited by 469 · see citing works
Measuring and improving the quality of mental health care: a global perspective
Kilbourne, A. M., Beck, K., Spaeth‐Rublee, B., Ramanuj, P., O'Brien, R. W., Tomoyasu, N., & Pincus, H. A. (2018) · World Psychiatry · doi:10.1002/wps.20482 · Cited by 457 · see citing works
The networked young citizen: social media, political participation and civic engagement
Loader, B. D., Vromen, A., & Xenos, M. A. (2014) · Information Communication & Society · doi:10.1080/1369118x.2013.871571 · Cited by 438 · see citing works
Use of Social Media in Health Promotion
Neiger, B. L., Thackeray, R., Wagenen, S. A. V., Hanson, C. L., West, J. H., Barnes, M., & Fagen, M. C. (2012) · Health Promotion Practice · doi:10.1177/1524839911433467 · Cited by 417 · see citing works
You Can't Stay Here: The Efficacy of Reddit's 2015 Ban Examined Through Hate Speech
Chandrasekharan, E., Pavalanathan, U., Srinivasan, A., Glynn, A., Eisenstein, J., & Gilbert, E. (2017) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3134666 · Cited by 390 · see citing works
Technological innovations in mental healthcare: harnessing the digital revolution
Hollis, C., Morriss, R., Martin, J., Amani, S., Cotton, R., Denis, M., & Lewis, S. (2015) · British Journal of Psychiatry · doi:10.1192/bjp.bp.113.142612 · Cited by 375 · see citing works
Tracking suicide risk factors through Twitter in the US.
Jashinsky, J., Burton, S. H., Hanson, C., West, J. H., Giraud-Carrier, C., Barnes, M. D., & Argyle, T. (2014) · Crisis · doi:10.1027/0227-5910/a000234 · Cited by 363 · see citing works
Gulliver, A., Griffiths, K. M., Christensen, H., Mackinnon, A., Calear, A. L., Parsons, A., Bennett, K., Batterham, P. J., & Stanimirovic, R. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1864 · Cited by 243 · see citing works
#WhyWeTweetMH: Understanding why people use twitter to discuss mental health problems
Berry, N., Lobban, F., Belousov, M., Emsley, R., Nenadic, G., & Bucci, S. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6173 · Cited by 228 · see citing works
Robinson, P., Turk, D., Jilka, S., & Cella, M. (2018) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-018-1571-5 · Cited by 223 · see citing works
Freeman, B., Potente, S., Rock, V., & McIver, J. (2015) · Public Health Research & Practice · doi:10.17061/phrp2521517 · Cited by 187 · see citing works
Birnbaum, M. L., Ernala, S. K., Rizvi, A. F., De Choudhury, M., & Kane, J. M. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7956 · Cited by 168 · cited 1× · see citing works
“…a billion users, over 2.5 billion active users of social media today, and with expanding penetration in high, medium, and low-income countries [7], these technologies can be ORIGINAL RESEARCH page 2 of 11 TBM construed to be a key element of any technology-facilitated mental health solution [8].”
BackgroundInfluentialSelf-citationUsing social media to monitor mental health discussions − evidence from Twitter
McClellan, C. B., Ali, M. M., Mutter, R. L., Kroutil, L. A., & Landwehr, J. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw133 · Cited by 141 · see citing works
Twitter as a Sentinel in Emergency Situations: Lessons from the Boston Marathon Explosions
Cassa, C. A., Chunara, R., Mandl, K. D., & Brownstein, J. S. (2013) · PLoS Currents · doi:10.1371/currents.dis.ad70cd1c8bc585e9470046cde334ee4b · Cited by 129 · see citing works
Modeling Stress with Social Media Around Incidents of Gun Violence on College Campuses
Saha, K., & De Choudhury, M. (2017) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3134727 · Cited by 124 · see citing works
Self-citationLaniado, D., & Mika, P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17746-0_30 · Cited by 113 · see citing works
Combining link-based and content-based methods for web document classification
Calado, P. P., de Cristo, M. A. P., de Moura, E. S., Ziviani, N., Ribeiro-Neto, B. A., & Gonçalves, M. A. (2003) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/956863.956938 · Cited by 98 · see citing works
#Schizophrenia: Use and misuse on Twitter
Joseph, A., Tandon, N., Yang, L. H., Duckworth, K., Torous, J. B., Seidman, L. J., & Keshavan, M. S. (2015) · Schizophrenia Research · doi:10.1016/j.schres.2015.04.009 · Cited by 93 · see citing works
Self-citationWhen Semi-supervised Learning Meets Ensemble Learning
Zhou, Z. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02326-2_53 · Cited by 86 · see citing works
Inferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments
Saha, K., Chan, L., De Barbaro, K., Abowd, G. D., & De Choudhury, M. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3130960 · Cited by 77 · see citing works
Self-citationBooth, R. G., Allen, B. N., Jenkyn, K. B., Li, L., & Shariff, S. Z. (2018) · JMIR Mental Health · doi:10.2196/mental.8808 · Cited by 74 · see citing works
Hswen, Y., Naslund, J. A., Brownstein, J. S., & Hawkins, J. B. (2018) · Psychiatric Quarterly · doi:10.1007/s11126-017-9559-y · Cited by 48 · see citing works
Analyzing Ideological Discourse on Social Media
Sharma, E., Saha, K., Ernala, S. K., Ghoshal, S., & Choudhury, M. D. (2017) · International Conference of the Computational Social Science Society of the Americas (CSSSA) · doi:10.1145/3145574.3145577 · Cited by 48 · see citing works
Self-citationSaha, K., Weber, I. G., Birnbaum, M. L., & Choudhury, M. D. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6815 · Cited by 39 · see citing works
Self-citationLiu, J., Zhao, S., & Wang, G. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.10.003 · Cited by 33 · see citing works
Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models
Paul, M. J., & Dredze, M. H. (2013) · Cited by 30 · see citing works
Mowery, D. L., Bryan, C. J., & Conway, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.08229 · Cited by 23 · see citing works
Twitter Streams Fuel Big Data Approaches to Health Forecasting
Kuehn, B. M. (2015) · JAMA · doi:10.1001/jama.2015.12836 · Cited by 20 · see citing works
Haskins, B. L., Davis-Martin, R. E., Abar, B. W., Baumann, B. M., Harralson, T. L., & Boudreaux, E. D. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6812 · Cited by 18 · see citing works
Predicting Multiple Risky Behaviors via Multimedia Content
Zhou, Y., Zhan, J., & Luo, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67256-4_7 · Cited by 17 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Eisenstein J, Ahmed A, Xing EP. Sparse additive generative models of text. In: Proceedings of the 28th International Conference on International Conference on Machine Learning. Omnipress. 2011:1041-1048.
Cited by
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 · see citing works
“A computational study of mental health awareness campaigns on Twitter reported that while stigmatizing content was rare, it was actually the most spread (re-tweeted) demonstrating that harmful content can travel quickly on social media (Saha et al. 2019).”
BackgroundSelf-citationPsychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media
Saha, K., Torous, J., Caine, E. D., & De Choudhury, M. (2020) · Journal of Medical Internet Research · doi:10.2196/22600 · Cited by 166 · see citing works
Self-citationA Social Media Study on the Effects of Psychiatric Medication Use
Saha, K., Sugar, B., Torous, J., Abrahao, B., Kıcıman, E., & De Choudhury, M. (2019) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v13i01.3242 · Cited by 96 · see citing works
Self-citation
- Authors
- Saha, K., Torous, J., Ernala, S. K., Rizuto, C., Stafford, A., & De Choudhury, M.
- Affiliations
- Georgia Institute of Technology (US)
- Beth Israel Deaconess Medical Center (US)
- School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, USA
- Corresponding author
- Torous, John
- Venue
- Translational Behavioral Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-03-05
- Volume
- 9
- Issue
- 6
- Pages
- 1197-1207
- ISSN
- 1613-9860 (ISSN-L)
- 1869-6716
- PMC
- PMC6875652
- OpenAlex
- W2919537623
- Cited by
- 93
- References
- 63
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 18 · 2023: 17 · 2022: 14 · 2021: 12 · 2020: 10 · 2019: 2
- FWCI
- 11.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Translational Behavioral Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (R01GM112697)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Health › Social Media in Health Education
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Keywords
- mental health awareness campaigns, social media, Twitter, World Mental Health Day, mental health stigma, online self-help, content classification, peer support, mental health resources
- MeSH terms
- Machine Learning, Classification, Delphi Technique, Health Promotion, Humans, Mental Health, Public Health, Social Media
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
