Social Media Authentication and Users’ Assessments of Health Information: Random Assignment Survey Experiment
Notice bibliographique
Résumé
BACKGROUND: In an effort to signal the authenticity of user accounts, social networking sites (SNSs) such as Facebook and X, formerly known as Twitter, use visual heuristics (blue checkmarks) to signify whether accounts are verified. While these verification badges are generally well recognized (and often coveted) by SNS users, relatively little is known about how they affect users' perceptions of accuracy or their likelihood of engaging with web-based information. This is particularly true in the case of information posted by medical experts and health care professionals. OBJECTIVE: This study aims to use an experimental survey design to assess the effect of these verification badges on SNS users' assessments of information accuracy as well as their proclivity to recirculate health information or follow verified medical experts in their social network. METHODS: A survey experiment using random assignment was conducted on a representative sample of 534 adult SNS users in Florida, United States. A total of 2 separate experimental scenarios exposed users to vaccine-related posts from verified medical experts on X. In each case, the original post contained a platform-issued verification badge (treatment group), which was subsequently edited out of the image as an experimental control. For each scenario, respondents were randomly assigned to either the treatment or control group, and responses to 3 follow-up questions were assessed through a series of chi-square analyses and 2 logit regression models. Responses were fielded using a stratified quota sampling approach to ensure representativeness of the state's population based on age, sex, race, ethnicity, and political affiliation. RESULTS: Users' assessments of information accuracy were not significantly impacted by the presence or absence of verification badges, and users exposed to the experimental treatment (verification badge) were not any more likely to repost the message or follow the author. While verification badges did not influence users' assessments or subsequent behaviors, reliance on social media for health-related information and political affiliation were substantial predictors of accuracy assessments in both experimental scenarios. In scenario 1, which included a post addressing COVID-19 vaccine efficacy, users who relied on social media "a great deal" for health information were 2 times more likely to assess the post as accurate (odds ratio 2.033, 95% CI 1.129-3.661; P=.01). In scenario 2, which included a post about measles vaccines, registered Republicans were nearly 6 times less likely to assess the post as accurate (odds ratio 0.171, 95% CI 0.097-0.299; P<.001). CONCLUSIONS: For health professionals and medical experts wishing to leverage social networks to combat misinformation and spread reliable health-related content, account verification appears to offer little by way of added value. On the basis of prior research, other heuristics and communication strategies are likely to yield better results.
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 | 0,036 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
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 ».