Online social networks provide users with unprecedented opportunities to engage with diverse opinions. At the same time, they enable confirmation bias on large scales by empowering individuals to self-select narratives they want to be exposed to. A precise understanding of such tradeoffs is still largely missing. We introduce a social learning model where most participants in a network update their beliefs unbiasedly based on new information, while a minority of participants reject information that is incongruent with their preexisting beliefs. This simple mechanism generates permanent opinion polarization and cascade dynamics, and accounts for the aforementioned tradeoff between confirmation bias and social connectivity through analytic results. We investigate the model's predictions empirically using US county-level data on the impact of Internet access on the formation of beliefs about global warming. We conclude by discussing policy implications of our model, highlighting the downsides of debunking and suggesting alternative strategies to contrast misinformation.

中文翻译：

---

当事实失败时：社交网络中的偏见、两极分化和真相

在线社交网络为用户提供了前所未有的机会来交流不同的意见。同时，它们使个人能够自主选择他们想要接触的叙述，从而在大规模上实现确认偏见。对这种权衡的准确理解仍然在很大程度上缺失。我们引入了一种社会学习模型，其中网络中的大多数参与者根据新信息无偏见地更新他们的信念，而少数参与者拒绝与他们先前存在的信念不一致的信息。这种简单的机制会产生永久性的意见两极分化和级联动态，并通过分析结果解释了上述确认偏差和社会连通性之间的权衡。我们调查模型' s 使用美国县级数据经验性地预测互联网访问对全球变暖信念形成的影响。我们最后讨论了我们模型的政策含义，强调了揭穿的缺点并提出了对比错误信息的替代策略。