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JMIR Mhealth Uhealth. 2019 Feb 12;7(2):e12264. doi: 10.2196/12264.

Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study.

Author information

1
Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA, United States.
2
Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.
3
Department of Mathematics and Statistics, Boston University, Boston, MA, United States.
4
Department of Pediatrics, Harvard Medical School, Boston, MA, United States.
5
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
#
Contributed equally

Abstract

BACKGROUND:

More 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.

OBJECTIVE:

This 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.

METHODS:

Data 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.

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). 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.

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 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.

KEYWORDS:

Twitter; autism; digital data; emotion; infodemiology; mobile phone; obsessive-compulsive disorder; social media; textual analysis; tweets

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