BACKGROUND
With 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.
OBJECTIVE
This study aimed to examine and identify the different behavioral patterns and interactions of ADHD users on Twitter through text content and metadata of their posted tweets.
METHODS
First, we built two datasets, an ADHD users dataset containing 3,135 users who explicitly reported having ADHD on Twitter, and a control dataset made up of 3,223 randomly selected Neurotypical Twitter users. All historical tweets of users in both datasets were collected. Then, we performed a comparison and analysis of topics, sentiments presented in users’ tweets, and the posting activities patterns between these two datasets.
RESULTS
In contrast to the control group of the Neurotypical dataset, ADHD users tweeted about the inability to concentrate and manage time, sleep disturbance, and drug abuse. ADHD users 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, ADHD users were more active in posting tweets (P=.04), especially at night between 12 a.m. to 6 a.m. (P<.001), posted more tweets with original content (P<.001), and tend to follow fewer people on Twitter (P<.001).
CONCLUSIONS
This study revealed how ADHD users behave and interact differently on Twitter compared to neurotypical users. Based on these differences, researchers, psychiatrics, 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 auto ADHD detection.