TikTok as a Source of Health Information and Misinformation for Young Women in the United States: Survey Study
Notice bibliographique
Résumé
Background TikTok is one of the most-used and fastest-growing social media platforms in the world, and recent reports indicate that it has become an increasingly popular source of news and information in the United States. These trends have important implications for public health because an abundance of health information exists on the platform. Women are among the largest group of TikTok users in the United States and may be especially affected by the dissemination of health information on TikTok. Prior research has shown that women are not only more likely to look for information on the internet but are also more likely to have their health-related behaviors and perceptions affected by their involvement with social media. Objective We conducted a survey of young women in the United States to better understand their use of TikTok for health information as well as their perceptions of TikTok’s health information and health communication sources. Methods A web-based survey of US women aged 18 to 29 years (N=1172) was conducted in April-May 2023. The sample was recruited from a Qualtrics research panel and 2 public universities in the United States. Results The results indicate that the majority of young women in the United States who have used TikTok have obtained health information from the platform either intentionally (672/1026, 65.5%) or unintentionally (948/1026, 92.4%). Age (959/1026, 93.47%; r=0.30; P<.001), education (959/1026, 93.47%; ρ=0.10; P=.001), and TikTok intensity (ie, participants’ emotional connectedness to TikTok and TikTok’s integration into their daily lives; 959/1026, 93.47%; r=0.32; P<.001) were positively correlated with overall credibility perceptions of the health information. Nearly the entire sample reported that they think that misinformation is prevalent on TikTok to at least some extent (1007/1026, 98.15%), but a third-person effect was found because the young women reported that they believe that other people are more susceptible to health misinformation on TikTok than they personally are (t1025=21.16; P<.001). Both health professionals and general users were common sources of health information on TikTok: 93.08% (955/1026) of the participants indicated that they had obtained health information from a health professional, and 93.86% (963/1026) indicated that they had obtained health information from a general user. The respondents showed greater preference for health information from health professionals (vs general users; t1025=23.75; P<.001); the respondents also reported obtaining health information from health professionals more often than from general users (t1025=8.13; P<.001), and they were more likely to act on health information from health professionals (vs general users; t1025=12.74; P<.001). Conclusions The findings suggest that health professionals and health communication scholars need to proactively consider using TikTok as a platform for disseminating health information to young women because young women are obtaining health information from TikTok and prefer information from health professionals.
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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».