Discussions of Asperger Syndrome on Social Media: Content and Sentiment Analysis on Twitter
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Notice bibliographique
Résumé
BACKGROUND: On May 8, 2021, Elon Musk, a well-recognized entrepreneur and business magnate, revealed on a popular television show that he has Asperger syndrome. Research has shown that people's perceptions of a condition are modified when influential individuals in society publicly disclose their diagnoses. It was anticipated that Musk's disclosure would contribute to discussions on the internet about the syndrome, and also to a potential change in the perception of this condition. OBJECTIVE: The objective of this study was to compare the types of information contained in popular tweets about Asperger syndrome as well as their engagement and sentiment before and after Musk's disclosure. METHODS: We extracted tweets that were published 1 week before and after Musk's disclosure that had received >30 likes and included the terms "Aspergers" or "Aspie." The content of each post was classified by 2 independent coders as to whether the information provided was valid, contained misinformation, or was neutral. Furthermore, we analyzed the engagement on these posts and the expressed sentiment by using the AFINN sentiment analysis tool. RESULTS: We extracted a total of 227 popular tweets (34 posted the week before Musk's announcement and 193 posted the week after). We classified 210 (92.5%) of the tweets as neutral, 13 (5.7%) tweets as informative, and 4 (1.8%) as containing misinformation. Both informative and misinformative tweets were posted after Musk's disclosure. Popular tweets posted before Musk's disclosure were significantly more engaging (received more comments, retweets, and likes) than the tweets posted the week after. We did not find a significant difference in the sentiment expressed in the tweets posted before and after the announcement. CONCLUSIONS: The use of social media platforms by health authorities, autism associations, and other stakeholders has the potential to increase the awareness and acceptance of knowledge about autism and Asperger syndrome. When prominent figures disclose their diagnoses, the number of posts about their particular condition tends to increase and thus promote a potential opportunity for greater outreach to the general public about that condition.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle