Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Sep 24, 2018
Open Peer Review Period: Sep 25, 2018 - Nov 2, 2018
Date Accepted: Nov 18, 2018
(closed for review but you can still tweet)
Feasibility of Using Twitter to Detect Psychological Characteristics of Self-Identified Persons with Autism Spectrum Disorder
ABSTRACT
Background:
More than 3.5 million Americans live with autism spectrum disorder (ASD). Major challenges persist in diagnosing ASD as no medical test to diagnose these disorders exist. Digital phenotyping holds promise to helping guide in the clinical diagnoses and screening of ASD.
Objective:
To explore the feasibility of using the online social media platform Twitter to detect psychological and behavioral characteristics of self-identified persons with ASD.
Methods:
Data from Twitter was retrieved from 152 self-identified users with ASD and 182 randomly selected control users from March 22, 2012 to July 20, 2017. A comparative textual analysis of tweets about repetitive and obsessive-compulsive behavioral characteristics typically associated with ASD was conducted between groups. Common emotional characteristics of persons with ASD such as fear, paranoia and anxiety were also examined between groups through textual analysis. Timing of tweets was compared between users with ASD and control users to identify patterns in communication.
Results:
Users with ASD posted a significantly higher frequency of tweets related to the specific repetitive behavior of counting compared with control users (P<.001). Textual analysis of obsessive-compulsive behavioral characteristics such as fixate, excessive, and concern were significantly higher among users with ASD compared with the control group (P<.001). Emotional terms related to fear, paranoia and anxiety were also tweeted at a significantly higher rate among users with ASD compared with control users (P<.001). Users with ASD posted a smaller proportion of tweets during time intervals of 00:00–05:59 (P<.001), 06:00–11:59 (P<.001), and 18:00-23.59 (P<.001), and a greater proportion of tweets from 12:00-17:59 (P<.001) compared with control users.
Conclusions:
Social media may be a valuable resource for observing unique psychological characteristics of self-identified persons with ASD. Collecting and analyzing data from these digital platforms may afford opportunities to identify characteristics of ASD and assist in the diagnosis or verification of autism spectrum disorders. This study highlights the feasibility of leveraging digital data for gaining new insights about various health conditions.
Citation
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Copyright
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