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Autism Spectrum and Developmental Disorders
Ingrid S. Tien, M.A.
PhD Student in Human Development & Psychology
University of California, Los Angeles
Los Angeles, California, United States
Samara M. Wolpe, M.A.
Ph.D. Student
University of California Los Angeles
Culver City, California, United States
Sila Sozeri, None
Student
UCLA
Los Angeles, California, United States
Maxwell Lee, None
Student
UCLA
Los Angeles, California, United States
Jay Seibold, None
Student
UCLA
Los Angeles, California, United States
Introduction: Recent years has seen a sharp increase in rates of self-diagnoses and acceptance of autism and neurodivergence amongst popular culture, particularly in social media. However, social media depictions and reports of autism are often not diagnostically accurate, with symptoms and experiences described online failing to align with the DSM-5 description of autism. This may indicate a disconnect between research findings and their connection to the community. Therefore, this study utilized a cross-section analysis of data to analyze social media content on autism, extracted from TikTok and Twitter.
Methods: Sampling content from both sites using autism-related hashtags, 268 TikToks and 450 Tweets were analyzed for source of the information, symptoms described, and disparities experienced. Themes emerged using inductive coding, which was then coded multicategorically by two independent coders who achieved interrater reliability. Symptom descriptions were then rated as diagnostically accurate or inaccurate.
Results: The majority of autism-related content was made by autistic adults discussing their own experiences. No correlation was found between content reported on disparities related to issues with the diagnostic process, discrimination, stereotypes, healthcare access, and more to any other category. Additionally, reporting repetitive and restrictive behaviors (RRBs) and social communication disorders (SCDs) were associated with diagnostically accurate symptoms (p < .005), but several non-diagnostically accurate traits (sensory sensitivity, high IQ, poor reactions, and specific personalities) were also significantly associated with the symptoms within the diagnostic criteria.
Discussion: These findings suggest a wide variety of disparities experienced by those reporting on social media, speaking to the diversity of the autistic community. These results further suggest that traits traditionally considered not diagnostically accurate may be associated with autism based on the experiences of the online autism community. These findings further suggest the higher collaboration and community engagement with the advocacy community to enhance future research findings.