Abstract
BackgroundMore than 3.5 million Americans live with autism spectrum disorder (ASD). Major challenges persist in diagnosing ASD as no medical test exists to diagnose this disorder. Digital phenotyping holds promise to guide in the clinical diagnoses and screening of ASD.ObjectiveThis study aims to explore the feasibility of using the Web-based social media platform Twitter to detect psychological and behavioral characteristics of self-identified persons with ASD.MethodsData from Twitter were retrieved from 152 self-identified users with ASD and 182 randomly selected control users from March 22, 2012 to July 20, 2017. We conducted a between-group comparative textual analysis of tweets about repetitive and obsessive-compulsive behavioral characteristics typically associated with ASD. In addition, common emotional characteristics of persons with ASD, such as fear, paranoia, and anxiety, were examined between groups through textual analysis. Furthermore, we compared the timing of tweets between users with ASD and control users to identify patterns in communication.ResultsUsers with ASD posted a significantly higher frequency of tweets related to the specific repetitive behavior of counting compared with control users (P<.001). The 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). In addition, emotional terms related to fear, paranoia, and anxiety were 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), as well as a greater proportion of tweets from 12:00 to 17:59 (P<.001) compared with control users.ConclusionsSocial 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 the characteristics of ASD and assist in the diagnosis or verification of ASD. This study highlights the feasibility of leveraging digital data for gaining new insights into various health conditions.
Highlights
Autism spectrum disorder (ASD) is a group of developmental disorders that affect >3.5 million Americans [1]
JMIR Mhealth Uhealth 2019 | vol 7 | iss. 2 | e12264 | p. 2 this study aims to compare the textual patterns of communication of Twitter users who self-identify as having autism spectrum disorder (ASD) to a general population of control users to identify textual signals related to fear, anxiety, and paranoia, which have often been recognized as a response to symptoms of ASD among individuals living with the disorder [12]
This study aims to explore common obsessive-compulsive disorder (OCD) behaviors using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) to understand whether these symptoms are present among Twitter users with ASD and whether the presence or absence of digital obsessive-compulsive symptoms could assist in reducing misdiagnoses of ASD
Summary
Autism spectrum disorder (ASD) is a group of developmental disorders that affect >3.5 million Americans [1]. Diagnosing ASD is difficult, as there is no medical test to diagnose it [6]. Symptoms of ASD can sometimes remain unnoticed in childhood as social and language impairments can be subtle and difficult to detect [9]. The Diagnostic and Statistical Manual of Mental Disorders (DSM-5), a widely used classification system, describes ASD as a disorder characterized by marked impairments in social interaction and communication accompanied by a pattern of restricted, repetitive behaviors and activities [10]. Many of these behaviors may be mistaken for other disorders. Digital phenotyping holds promise to guide in the clinical diagnoses and screening of ASD
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