ArticleJournal of medical Internet research2023
Exploring the Behavior of Users With Attention-Deficit/Hyperactivity Disorder on Twitter: Comparative Analysis of Tweet Content and User Interactions.
Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 12 citations in OpenAlex.
- Evaluating Public Sentiment on Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Compared With Other Mental Health Disorders From Posts on X (Formerly Known as Twitter): Longitudinal Analysis.JMIR infodemiology · 2026Article
- Unveiling Social Media Content Related to ADHD Treatment: Machine Learning Study Using X's Posts over 15 Years.Healthcare (Basel, Switzerland) · 2025Article
- Predicting Behaviour Patterns in Online and PDF Magazines with AI Eye-Tracking.Behavioral sciences (Basel, Switzerland) · 2024Article
- Leveraging the Black Girls Run Web-Based Community as a Supportive Community for Physical Activity Engagement: Mixed Methods Study.JMIR formative research · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the widespread use of social media, people share their real-time thoughts and feelings via interactions on these platforms, including those revolving around mental health problems. This can provide a new opportunity for researchers to collect health-related data to study and analyze mental disorders. However, as one of the most common mental disorders, there are few studies regarding the manifestations of attention-deficit/hyperactivity disorder (ADHD) on social media.
objectiveThis study aims to examine and identify the different behavioral patterns and interactions of users with ADHD on Twitter through the text content and metadata of their posted tweets.
methodsFirst, we built 2 data sets: an ADHD user data set containing 3135 users who explicitly reported having ADHD on Twitter and a control data set made up of 3223 randomly selected Twitter users without ADHD. All historical tweets of users in both data sets were collected. We applied mixed methods in this study. We performed Top2Vec topic modeling to extract topics frequently mentioned by users with ADHD and those without ADHD and used thematic analysis to further compare the differences in contents that were discussed by the 2 groups under these topics. We used a distillBERT sentiment analysis model to calculate the sentiment scores for the emotion categories and compared the sentiment intensity and frequency. Finally, we extracted users' posting time, tweet categories, and the number of followers and followings from the metadata of tweets and compared the statistical distribution of these features between ADHD and non-ADHD groups.
resultsIn contrast to the control group of the non-ADHD data set, users with ADHD tweeted about the inability to concentrate and manage time, sleep disturbance, and drug abuse. Users with ADHD felt confusion and annoyance more frequently, while they felt less excitement, caring, and curiosity (all P<.001). Users with ADHD were more sensitive to emotions and felt more intense feelings of nervousness, sadness, confusion, anger, and amusement (all P<.001). As for the posting characteristics, compared with controls, users with ADHD were more active in posting tweets (P=.04), especially at night between midnight and 6 AM (P<.001); posting more tweets with original content (P<.001); and following fewer people on Twitter (P<.001).
conclusionsThis study revealed how users with ADHD behave and interact differently on Twitter compared with those without ADHD. On the basis of these differences, researchers, psychiatrists, and clinicians can use Twitter as a potentially powerful platform to monitor and study people with ADHD, provide additional health care support to them, improve the diagnostic criteria of ADHD, and design complementary tools for automatic ADHD detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.