The Influence of Social Media on the Perception of Autism Spectrum Disorders: Content Analysis of Public Discourse on YouTube Videos
Bibliographic record
Abstract
BACKGROUND: Little is known about how social media shapes the public's perception of autism spectrum disorder (ASD). We used a media content analysis approach to analyze the public's perception of ASD. METHODS: We conducted a YouTube search in 2019 using keywords related to ASD. The first 10 videos displayed after each search that met the eligibility criteria were selected for analysis. The final sample size of videos analyzed was 50. The top 10 comments from each respective video were selected for commentary analysis. A total of 500 comments were used for this study. Videos and comments were categorized based on sentiment, evident themes, and subthemes. In 2022, using the same key words, we conducted a subsequent YouTube search using the same criteria, except that the videos had to be 10 min or less, whereby nine videos were selected out of 70 for commentary analysis, and a total of 180 comments were used. RESULTS: The dominant themes were "providing educational information on ASD characteristics" with the main subtheme being "no specific age or sex focus". The most common category of comments was "anecdote". The overwhelming sentiments of both the videos and comments were "mixed". Individuals with ASD were stigmatized as not being able to understand emotion. Furthermore, ASD was also stigmatized as being a monolithic condition only manifesting itself in the most severe form when autism varies in severity. INTERPRETATION: YouTube is a powerful tool that allows people and organizations to raise awareness about ASD by providing a more dynamic view on autism and fostering an environment for public empathy and support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".