| Autor: |
Hswen, Yulin, Gopaluni, Anuraag, Brownstein, John S, Hawkins, Jared B |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
JMIR mHealth and uHealth, Vol 7, Iss 2, p e12264 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2291-5222 |
| DOI: |
10.2196/12264 |
| Popis: |
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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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