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

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

Abstract read

In one paragraph

Article in Journal of autism and developmental disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ingrid TienCentre for Addiction and Mental Health, McCain Centre for Child, Youth, and Family Mental Health, Toronto, Ontario, Canada. istien@g.ucla.edu.ORCID http://orcid.org/0000-0003-1565-9214

Samara WolpeDepartment of Psychiatry, University of California, Los Angeles, USA.ORCID http://orcid.org/0009-0000-3688-3122

Yingshi HuangDepartment of Education, University of California, Los Angeles, USA.

Sila SozeriDepartment of Psychology, University of California, Los Angeles, USA.

Maxwell LeeDepartment of Psychology, University of California, Los Angeles, USA.

Minjeon JeongDepartment of Education, University of California, Los Angeles, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIncreasing 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.

methodsWritten, 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.

resultsSurprisingly, 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.

conclusionThese 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.

Indexed as

AutismDiagnosisSelf-diagnosisSelf-identificationSocial media

Identifiers

PMID41335149

DOI10.5296/jei.v2i2.9975

What Socratic holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.