Does Eating Chicken Feet With Pickled Peppers Cause Avian Influenza? Observational Case Study on Chinese Social Media During the Avian Influenza A (H7N9) Outbreak
Notice bibliographique
Résumé
BACKGROUND: A hot topic on the relationship between a popular avian-origin food and avian influenza occurred on social media during the outbreak of the emerging avian influenza A (H7N9). The misinformation generated from this topic had caused great confusion and public concern. OBJECTIVE: Our goals were to analyze the trend and contents of the relevant posts during the outbreak. We also aimed to understand the characteristics of the misinformation and to provide suggestions to reduce public misconception on social media during the emerging disease outbreak. METHODS: The original microblog posts were collected from China's Sina Weibo and Tencent Weibo using a combination of keywords between April 1, 2013 and June 2, 2013. We analyzed the weekly and daily trend of the relevant posts. Content analyses were applied to categorize the posts into 4 types with unified sorting criteria. The posts' characteristics and geographic locations were also analyzed in each category. We conducted further analysis on the top 5 most popular misleading posts. RESULTS: A total of 1680 original microblog posts on the topic were retrieved and 341 (20.30%) of these posts were categorized as misleading messages. The number of relevant posts had not increased much during the first 2 weeks but rose to a high level in the next 2 weeks after the sudden increase in number of reported cases at the beginning of week 3. The posts under "misleading messages" occurred and increased from the beginning of week 3, but their daily posting number decreased when the daily number of posts under "refuting messages" outnumbered them. The microbloggers of the misleading posts had the lowest mean rank of followers and previous posts, but their posts had a highest mean rank of posts. The proportion of "misleading messages" in places with no reported cases was significantly higher than that in the epidemic areas (23.6% vs 13.8%). The popular misleading posts appeared to be short and consisted of personal narratives, which were easily disseminated on social media. CONCLUSIONS: Our findings suggested the importance of responding to common questions and misconceptions on social media platforms from the beginning of disease outbreaks. Authorities need to release clear and reliable information related to the popular topics early on. The microbloggers posting correct information should be empowered and their posts could be promoted to clarify false information. Equal importance should be attached to clarify misinformation in both the outbreak and nonoutbreak areas.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».