ArticleJMIR mHealth and uHealth2019
Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study.
Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Leveraging social media data to study disease and treatment characteristics of Hodgkin's lymphoma Using Natural Language Processing methods.PLOS digital health · 2025Article
- Using Longitudinal Twitter Data for Digital Epidemiology of Childhood Health Outcomes: An Annotated Data Set and Deep Neural Network Classifiers.Journal of medical Internet research · 2024Article
- Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study.JMIR formative research · 2024Article
- Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action.International journal of environmental research and public health · 2023Article
- Qualitative analysis of mental health conversational agents messages about autism spectrum disorder: a call for action.Frontiers in digital health · 2023Article
- Popular and Scientific Discourse on Autism: Representational Cross-Cultural Analysis of Epistemic Communities to Inform Policy and Practice.Journal of medical Internet research · 2022Article
- Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health.Annual review of public health · 2022Review
- Machine learning of language use on Twitter reveals weak and non-specific predictions.NPJ digital medicine · 2022Article
- Emotion diffusion effect: Negative sentiment COVID-19 tweets of public organizations attract more responses from followers.PloS one · 2022Article
- Assessing vulnerability to psychological distress during the COVID-19 pandemic through the analysis of microblogging content.Future generations computer systems : FGCS · 2021Article
- Aggregating Twitter Text through Generalized Linear Regression Models for Tweet Popularity Prediction and Automatic Topic Classification.European journal of investigation in health, psychology and education · 2021Article
- Using Patient-Generated Health Data From Twitter to Identify, Engage, and Recruit Cancer Survivors in Clinical Trials in Los Angeles County: Evaluation of a Feasibility Study.JMIR formative research · 2021Article
- Exploring discussions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic mandate implementation: A Twitter analysis.SSM - population health · 2021Article
- Expressed Symptoms and Attitudes Toward Using Twitter for Health Care Engagement Among Patients With Lupus on Social Media: Protocol for a Mixed Methods Study.JMIR research protocols · 2021Article
- Benefits and Risks: A Systematic Review of Information and Communication Technology Use by Autistic People.Autism in adulthood · 2021Article
- Perspectives Toward Seeking Treatment Among Patients With Psoriasis: Protocol for a Twitter Content Analysis.JMIR research protocols · 2021Article
- Insights From Twitter Conversations on Lupus and Reproductive Health: Protocol for a Content Analysis.JMIR research protocols · 2020Article
- Racial and Ethnic Disparities in Patient Experiences in the United States: 4-Year Content Analysis of Twitter.Journal of medical Internet research · 2020Article
- The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective.Frontiers in psychology · 2020Article
- Google Trends in Infodemiology and Infoveillance: Methodology Framework.JMIR public health and surveillance · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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