论文标题
概率社会学习改善了公众对错误信息的发现
Probabilistic Social Learning Improves the Public's Detection of Misinformation
论文作者
论文摘要
错误信息的数字传播是对民主，公共卫生和全球经济的主要威胁之一。缓解错误信息的流行策略包括众包，机器学习和媒体素养计划，这些计划要求社交媒体用户用二进制术语将新闻分类为真或错误。但是，对同伴影响的研究表明，二进制术语的框架决策可以放大判断错误并限制社会学习，而概率术语的框架决定可以可靠地改善判断。在此预先进行的实验中，我们比较在线同行网络，该网络通过传达二进制或概率判断来协作评估新闻的真实性。交换新闻真实性的概率估计值大大改善了个人和群体判断，并消除了新闻评估的两极分化。相比之下，交换二元分类减少了社会学习和根深蒂固的两极分化。概率社会学习的好处对参与者的教育，性别，种族，收入，宗教和党派都是可靠的。
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 evaluate 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 entrenched polarization. The benefits of probabilistic social learning are robust to participants' education, gender, race, income, religion, and partisanship.
