Visual Prebunking Advertisements Perform Better Than Their Audio-Only Counterpart for Improving Information Literacy
Notice bibliographique
Résumé
A Review of: Daly, D., & Jarrette, K. (2025). Design of audio ads to prebunk misinformation and promote civil discourse. Information Research: An International Electronic Journal, 30(iConf), 249–259. https://doi.org/10.47989/ir30iConf47359 Objective – To determine if the use of prebunking advertisements influences information literacy, the ability to identify false news headlines, or attitudes toward civil discourse. Design – A pilot, experimental study. Setting – A large university in the southwestern United States. Subjects – 143 undergraduate students. Methods – A research team developed five short audio advertisements intended for prebunking sources of misinformation identified through social media. For each misinformation strategy, the team created a humorous sketch, dramatizing an interaction between two characters who knew each other. The team created familiar characters to model how one could engage friends or family who could be susceptible to believing misinformation and promote civil discourse among them. The audio ads were intended to be aired during podcasts known to spread misinformation. For the experimental design, the audio ads were coupled with Artificial Intelligence (AI)-generated visualization. Researchers set out to determine whether exposure to a specific prebunking ad enhances an individual’s ability to identify false news headlines, whether the visualization of the ad script using AI assistance impacts respondent literacy, and how participants describe and gauge the effectiveness of a specific prebunking audio ad. Participants were recruited through instructors who taught courses related to study topics. Instructors were encouraged to offer extra credit for participation. In Part 1 of the study, participants answered questions about demographics and social media use. Participants completed two established qualitative questionnaires: the Generic Conspiracist Beliefs scale (GCBS) and the Misinformation Susceptibility Test (MIST-20) (Maertens et al., 2024). The researchers developed a questionnaire modeled after the MIST-20, the ITMIST, using real and fake headlines. Participants were exposed to one ad: either an audio-only ad, an AI-generated visualization ad, or a control ad. Participants completed another qualitative questionnaire after viewing the ad to finish Part 1. The following day, participants received a link to complete the GCBS, MIST-20, and ITMIST and completed another qualitative questionnaire within a week of the first survey, to finish Part 2 of the study. Main Results – One hundred forty-three participants completed Part 1 of the study, and 99 completed Part 2. Participants ranged in age from 18–48 years; 59.6% identified as female, 38.4% identified as male; 54.5% identified as White/Caucasian, with the remaining participants identifying as racially diverse; 34.4% identified as Democrat, 32.3% Republican, 18.2% Independent; and participants represented multiple religious affiliations. All participants used a social media platform at least once a week: 43.4% reported usage over two hours per day, 26.3% between 90–120 minutes, 12.1% between 60–90 minutes, 14.1% between 30–60 minutes, and 4% less than 30 minutes. Nearly 90 percent (89.9) of participants used Instagram, 67.6% TikTok, 66.7% Snapchat, 34.3% Twitter/X, 21.2% Facebook, and 8.1% used other social media platforms. Regarding podcasts, 23.2% frequently tuned in, 50.5% sometimes tuned in, and 26.3% never tuned in. Of those who listened to podcasts, 71.2% always skipped podcast ads, 26% sometimes skipped, and 1.4% never skipped. The podcasts that participants reported frequently tuning into for entertainment and education were strongly related to stated political affiliation. The authors reported the results of the MIST-20 and ITMIST in this article. At the time of publication, the authors were still analyzing the results of the GCBS and the complete quantitative and qualitative data. When comparing the AI-generated visualization ad (Visual Experimental group) to the Visual Control group, investigators reported a significantly large average improvement in information literacy scores for the Experimental group on the MIST-20 (Visual Experimental x̄ = 0.93, Visual Control x̄ = 0.33), and a moderate average improvement on the ITMIST (Visual Experimental x̄ = 0.98, Visual Control x̄ = 0.81). When comparing the Audio Experimental group to the Audio Control group, investigators report mixed results. The Audio Experimental group did not show as great an average improvement compared to the Control group on the MIST-20 (Audio Experimental x̄ = 0.85, Audio Control x̄ = 1.41) but scored higher than the Control group on the ITMIST (Audio Experimental x̄ = 0.78, Audio Control x̄ = 0.45). More than half of the participants in each Experimental group improved in score. Those who improved showed a greater change in score than those whose score declined. Conclusion – Prebunking ads improved information literacy, but a greater improvement was shown with AI-generated visualization ads than with audio-only ads. The investigators acknowledge the benefit of theatrical visual advertisements to prebunk misinformation and plan research to include broader populations.
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 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,003 |
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 ».