In recent years, scholars have raised concerns on the effects that unreliable news, or "fake news," has on our political sphere, and our democracy as a whole. For example, the propagation of fake news on social media is widely believed to have influenced the outcome of national elections, including the 2016 U.S. Presidential Election, and the 2020 COVID-19 pandemic. What drives the propagation of fake news on an individual level, and which interventions could effectively reduce the propagation rate? Our model disentangles bias from truthfulness of an article and examines the relationship between these two parameters and a reader's own beliefs. Using the model, we create policy recommendations for both social media platforms and individual social media users to reduce the spread of untruthful or highly biased news. We recommend that platforms sponsor unbiased truthful news, focus fact-checking efforts on mild to moderately biased news, recommend friend suggestions across the political spectrum, and provide users with reports about the political alignment of their feed. We recommend that individual social media users fact check news that strongly aligns with their political bias and read articles of opposing political bias.

中文翻译：

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假新闻的流行和传播

近年来，学者们对不可靠的新闻或“假新闻”对我们的政治领域和整个民主的影响提出了担忧。例如，人们普遍认为社交媒体上假新闻的传播影响了全国选举的结果，包括 2016 年美国总统大选和 2020 年 COVID-19 大流行。是什么推动了假新闻在个人层面的传播，哪些干预措施可以有效降低传播率？我们的模型将偏见与文章的真实性分开，并检查这两个参数与读者自己的信念之间的关系。使用该模型，我们为社交媒体平台和个人社交媒体用户创建政策建议，以减少不真实或高度偏见的新闻的传播。我们建议平台赞助公正的真实新闻，将事实核查工作重点放在带有轻微到中度偏见的新闻上，推荐跨政治领域的朋友建议，并向用户提供有关其供稿的政治一致性的报告。我们建议个人社交媒体用户查看与其政治偏见强烈一致的新闻，并阅读反对政治偏见的文章。