Investigating the Role of Communication for Information Seekers’ Trust-Related Evaluations of Health Videos on the Web: Content Analysis, Survey Data, and Experiment
Notice bibliographique
Résumé
Background: According to the language expectancy theory and the communication accommodation theory, health information seekers’ trust evaluations of Web-based videos are determined by interplays between content and seekers’ expectations on vloggers’ appropriate language use in specific contexts of Web-based communication. Objectives: Two investigations focused on differences both between vloggers’ language styles and between users’ general trust in specific Web-based platforms to investigate how the context of Web-based communication can be characterized (research question, RQ1). Thereafter, we investigated whether information uncertainty, vloggers’ language style, and context of Web-based communication affect seekers’ trust evaluations of videos (RQ2). Methods: With a content analysis of 36 health videos from YouTube and Vimeo, we examined the extent of trust-related linguistic characteristics (ie, first-person and second-person pronouns). Additionally, we surveyed participants (n=151) on their trust in YouTube and Moodle (academic Web-based platform; RQ1). In an experiment, further participants (n=124) watched a video about nutrition myths and were asked to evaluate the information credibility, vloggers’ trustworthiness, and accommodation of language by vloggers (RQ2). Following a 3 × 2 × 2 mixed design, vloggers’ explanations contained unambiguous (confirming or disconfirming) or ambiguous (neither confirming nor disconfirming) evidence on the myths (within factor). Furthermore, vloggers used YouTube-typical language (many first-person pronouns) or formal language (no first-person pronouns), and videos were presented on YouTube or Moodle (between factors). Results: The content analysis revealed that videos on YouTube contained more first-person pronouns than on Vimeo (F1,35=4.64; P=.04; ηp 2=0.12), but no more second-person pronouns (F1,35=1.23; P=.23). Furthermore, when asked about their trust in YouTube or Moodle, participants trusted YouTube more than Moodle (t150≤−9.63; all P≤.001). In the experiment, participants evaluated information to be more credible when information contained unambiguous rather than ambiguous evidence (F2,116=9.109; P.34). However, participants judged vloggers who used a YouTube-typical language as being more benevolent, and their language use as being more appropriate in both video platforms (F1,117≥3.41; P≤.03; ηp 2≥0.028). Moreover, participants rated the YouTube-typical (vs formal) language as more appropriate for Moodle, but they did not rate one or the other language style as more appropriate for YouTube (F1,117=5.40; P=.01; ηp 2=0.04). Conclusions: This study shows that among specific Web-based contexts, users’ typical language use can differ, as can their trust-related evaluations. In addition, health information seekers seem to be affected by providers’ language styles in ways that depend on the Web-based communication context. Accordingly, further investigations that would identify concrete interplays between language style and communication context might help providers to understand whether additional information would help or hurt seekers’ ability to accurately evaluate information.
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 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 | 0,100 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».