Probabilistic social learning improves the public's judgments of news veracity.
PLoS One
; 16(3): e0247487, 2021.
Article
en En
| MEDLINE
| ID: mdl-33690668
The digital spread of misinformation is one of the leading threats to democracy, public health, and the global economy. Popular strategies for mitigating misinformation include crowdsourcing, machine learning, and media literacy programs that require social media users to classify news in binary terms as either true or false. However, research on peer influence suggests that framing decisions in binary terms can amplify judgment errors and limit social learning, whereas framing decisions in probabilistic terms can reliably improve judgments. In this preregistered experiment, we compare online peer networks that collaboratively evaluated the veracity of news by communicating either binary or probabilistic judgments. Exchanging probabilistic estimates of news veracity substantially improved individual and group judgments, with the effect of eliminating polarization in news evaluation. By contrast, exchanging binary classifications reduced social learning and maintained polarization. The benefits of probabilistic social learning are robust to participants' education, gender, race, income, religion, and partisanship.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
/
Comunicación
/
Medios de Comunicación Sociales
/
Aprendizaje Social
/
Juicio
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Adult
/
Female
/
Humans
/
Male
País/Región como asunto:
America do norte
Idioma:
En
Revista:
PLoS One
Asunto de la revista:
CIENCIA
/
MEDICINA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos