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Purpose <p>Increasing social media usage for information-seeking and increasing discussion of autism on social media platforms has been associated with increased awareness of autism. While online conversation about autistic experiences continue to grow, so does the prevalence of autism (Harrop et al., <CitationRef CitationID="CR55">2024</CitationRef>). Therefore, there is a call for research examining the quantity and quality of non-clinically driven social media content.</p> Methods <p>Written, audio, and visual content from 597 TikToks and 596 Tweets were pulled and inductively coded for content on autism-related difficulties, disparities, source of content, and alignment with the current diagnostic criteria.</p> Results <p>Surprisingly, only 20.4% of content collected contained solely diagnostically accurate information, indicating a potentially poor alignment of diagnostic criteria and experiences expressed in social media. To understand how both diagnostically accurate and inaccurate difficulties are represented on this content, a novel latent space modeling methodology was used to generate a picture of the most frequently endorsed items.</p> Conclusion <p>These items, mainly including non-diagnostic items, with the only diagnostic items being associated with social and communicative difficulties, indicate that the primary social media portrayal of autism is not currently aligned with our clinical definition of autism.</p>

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How Diagnostically Accurate are #Autism Portrayals? A Latent Space Item Response Modeling Approach

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Ingrid Tien,
  • Samara Wolpe,
  • Yingshi Huang,
  • Sila Sozeri,
  • Maxwell Lee,
  • Minjeon Jeong

摘要

Purpose

Increasing social media usage for information-seeking and increasing discussion of autism on social media platforms has been associated with increased awareness of autism. While online conversation about autistic experiences continue to grow, so does the prevalence of autism (Harrop et al., 2024). Therefore, there is a call for research examining the quantity and quality of non-clinically driven social media content.

Methods

Written, audio, and visual content from 597 TikToks and 596 Tweets were pulled and inductively coded for content on autism-related difficulties, disparities, source of content, and alignment with the current diagnostic criteria.

Results

Surprisingly, only 20.4% of content collected contained solely diagnostically accurate information, indicating a potentially poor alignment of diagnostic criteria and experiences expressed in social media. To understand how both diagnostically accurate and inaccurate difficulties are represented on this content, a novel latent space modeling methodology was used to generate a picture of the most frequently endorsed items.

Conclusion

These items, mainly including non-diagnostic items, with the only diagnostic items being associated with social and communicative difficulties, indicate that the primary social media portrayal of autism is not currently aligned with our clinical definition of autism.