Language of ADHD in Adults on Social Media
Abstract
Objective: We computationally analyze the language of social media users diagnosed with ADHD to understand what they talk about, and how their language is correlated with users’ characteristics such as personality and temporal orientation. Method: We analyzed approximately 1.3 million tweets written by 1,399 Twitter users with self-reported diagnoses of ADHD, comparing their posts with those used by a control set matched by age, gender, and period of activity. Results: Users with ADHD are found to be less agreeable, more open, to post more often, and to use more negations, hedging, and swear words. Posts are suggestive of themes of emotional dysregulation, self-criticism, substance abuse, and exhaustion. A machine learning model can predict which of these Twitter users has ADHD with an out-of-sample AUC of .836. Conclusion: Based on this emerging technology, conjectures of future uses of social media by researchers and clinicians to better understand the naturalistic manifestations and sequelae of ADHD.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
People with ADHD on Twitter have been found to post more often than those without ADHD and post about themes of emotional-dysregulation, self-criticism, substance use, and exhaustion.
“People with ADHD on Twitter have been found to post more often than those without ADHD and post about themes of emotional-dysregulation, self-criticism, substance use, and exhaustion (Guntuku et al., 2019).”
From The experiences of adults with ADHD in interpersonal relationships and online communities: A qualitative study (2023) · cites this paper as Guntuku et al., 2019
Cites
A very brief measure of the Big-Five personality domains
Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003) · Journal of Research in Personality · doi:10.1016/s0092-6566(03)00046-1 · Cited by 8,307 · see citing works
Attention-deficit/hyperactivity disorder
Faraone, S. V., Asherson, P., Banaschewski, T., Biederman, J., Buitelaar, J. K., Ramos-Quiroga, J. A., Rohde, L. A., Sonuga-Barke, E. J. S., Tannock, R., & Franke, B. (2015) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2015.20 · Cited by 1,479 · see citing works
Under Diagnosis of Adult ADHD: Cultural Influences and Societal Burden
Asherson, P., Akehurst, R., Kooij, J. J. S., Huss, M., Beusterien, K., Sasané, R., Gholizadeh, S., & Hodgkins, P. (2012) · Journal of Attention Disorders · doi:10.1177/1087054711435360 · Cited by 189 · see citing works
ADHD and personality: a meta-analytic review
Gomez, R., & Corr, P. J. (2014) · Clinical Psychology Review · doi:10.1016/j.cpr.2014.05.002 · Cited by 151 · see citing works
Surman, C. B. H., Biederman, J., Spencer, T., Yorks, D., Miller, C. A., Petty, C. R., & Faraone, S. V. (2011) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.10081172 · Cited by 135 · cited 2× · see citing works
“Most of the LDA topics have messages relating to lack of focus and self-regulation (Barkley, 2010), emotional dysregulation (Faraone et al., 2015; Surman et al., 2011), intention and failure, negation, self-criticism, expressions of mental, physical, and emotional exhaustion.”
BackgroundKnouse, L. E., Zvorsky, I., & Safren, S. A. (2013) · Cognitive Therapy and Research · doi:10.1007/s10608-013-9569-5 · Cited by 78 · see citing works
Are negative automatic thoughts associated with ADHD in adulthood?
Mitchell, J. T., Benson, J. W., Knouse, L. E., Kimbrel, N. A., & Anastopoulos, A. D. (2013) · Cognitive Therapy and Research · doi:10.1007/s10608-013-9525-4 · Cited by 61 · see citing works
Fatigue in an adult attention deficit hyperactivity disorder population: A trans‐diagnostic approach
Rogers, D. C., Dittner, A. J., Rimes, K. A., & Chalder, T. (2017) · British Journal of Clinical Psychology · doi:10.1111/bjc.12119 · Cited by 50 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Psychosocial 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
Conway, M., Hu, M., & Chapman, W. W. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677918 · Cited by 91 · see citing works
Ginapp, C. M., Greenberg, N. R., Macdonald-Gagnon, G., Angarita, G. A., Bold, K. W., & Potenza, M. N. (2023) · SSM - Qualitative Research in Health · doi:10.1016/j.ssmqr.2023.100223 · Cited by 67 · see citing works
- Authors
- Guntuku, S. C., Ramsay, J. R., Merchant, R. M., & Ungar, L. H.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Guntuku, Sharath Chandra
- Venue
- Journal of Attention Disorders
- Publisher
- SAGE Publications
- Published
- 2017-11-08
- Volume
- 23
- Issue
- 12
- Pages
- 1475-1485
- ISSN
- 1087-0547 (ISSN-L)
- 1557-1246
- OpenAlex
- W2768073594
- Semantic Scholar
- 38f7bf18a6aaf8fcd3b058abbd1fe654cee8f19c
- Cited by
- 89
- References
- 8
- Citations by year
- 2026: 12 · 2025: 7 · 2024: 15 · 2023: 16 · 2022: 10 · 2021: 5 · 2020: 8 · 2019: 9 · 2018: 7
- FWCI
- 9.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Attention Disorders (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Keywords
- attention deficit hyperactivity disorder, adult ADHD, social media language, Twitter, self-reported diagnosis, personality traits, time orientation, emotional dysregulation, self-criticism, substance abuse, exhaustion, swearing, computational text analysis
- MeSH terms
- Adult, Attention Deficit Disorder with Hyperactivity, Emotions, Humans, Language, 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
