Developing an accuracy-prompt toolkit to reduce COVID-19 misinformation online
Notice bibliographique
Résumé
Recent research suggests that shifting users’ attention to accuracy increases the quality of news they subsequently share online. Here we help develop this initial observation into a suite of deployable interventions for practitioners. We ask (i) how prior results generalize to other approaches for prompting users to consider accuracy, and (ii) for whom these prompts are more versus less effective. In a large survey experiment examining participants’ intentions to share true and false headlines about COVID-19, we identify a variety of different accuracy prompts that successfully increase sharing discernment across a wide range of demographic subgroups while maintaining user autonomy. Research questions•There is mounting evidence that inattention to accuracy plays an important role in the spread of misinformation online. Here we examine the utility of a suite of different accuracy prompts aimed at increasing the quality of news shared by social media users.•Which approaches to shifting attention towards accuracy are most effective? •Does the effectiveness of the accuracy prompts vary based on social media user characteristics? Assessing effectiveness across subgroups is practically important for examining the generalizability of the treatments, and is theoretically important for exploring the underlying mechanism.Essay summary•Using survey experiments with N=9,070 American social media users (quota-matched to the national distribution on age, gender, ethnicity, and geographic region), we compared the effect of different treatments designed to induce people to think about accuracy when deciding what news to share. Participants received one of the treatments (or were assigned to a control condition), and then indicated how likely they would be to share a series of true and false news posts about COVID-19. •We identified three lightweight, easily-implementable approaches that each increased sharing discernment (the quality of news shared, measured as the difference in sharing probability of true versus false headlines) by roughly 50%, and a slightly more lengthy approach that increased sharing discernment by close to 100%. We also found that another approach that seemed promising ex ante (descriptive norms) was ineffective. Further-more, gender, race, partisanship, and concern about COVID-19 did not moderate effectiveness, suggesting that the accuracy prompts will be effective for a wide range of demographic subgroups. Finally, helping to illuminate the mechanism behind the effect, the prompts were more effective for participants who were more attentive, reflective, engaged with COVID-related news, concerned about accuracy, college-educated, and middle-aged. •From a practical perspective, our results suggest a menu of accuracy prompts that are effective in our experimental setting and that technology companies could consider testing on their own services.
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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 | 0,001 | 0,008 |
| 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 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 tête enseignante, 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 ».