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 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. Methods First, 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. Results In 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). Conclusions This 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.
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.
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.
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