ABSTRACT Most people receive political news from social media platforms. Unlike traditional media, these platforms algorithmically determine the order in which news is presented, in part relying on an article’s popularity (i.e. number of likes, shares, and comments) to determine its ranking. To what extent does sorting the news by popularity influence people’s attitudes toward politics? With two large, nationally representative samples of adults and a novel experimental design, we find that ranking news articles by their popularity has consequential effects that vary depending on the partisan composition of the group upon which the rankings are based. Overall, algorithmic sorting exacerbates the tendency to “like” news that conform to the dominant viewpoint of the reference group. For example, when the group is ideologically like-minded, the in-party bias in “liking” creates a self-perpetuating cycle, where attitudinally congruent articles remain at the top of the feed. When the reference group is rather composed of out-party members, politically incongruent items rise to the top. While this helps to expose readers to new information, they also become disengaged from reading and sharing political news. Overall, our study holds important implications for how using social media as an outlet for political information can influence – and even polarize – our political preferences.

中文翻译：

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新闻排序：人气排名如何两极分化我们的政治

摘要 大多数人从社交媒体平台接收政治新闻。与传统媒体不同，这些平台通过算法确定新闻呈现的顺序，部分依赖于文章的受欢迎程度（即喜欢、分享和评论的数量）来确定其排名。按受欢迎程度排序新闻在多大程度上影响人们对政治的态度？通过两个具有全国代表性的大型成人样本和新颖的实验设计，我们发现根据新闻文章的受欢迎程度对新闻文章进行排名会产生相应的影响，这些影响取决于排名所依据的群体的党派组成。总的来说，算法排序加剧了“喜欢”符合参考群体主导观点的新闻的趋势。例如，当这个群体在意识形态上志同道合时，“喜欢”的党内偏见会形成一个自我延续的循环，在这种循环中，态度一致的文章仍然位于提要的顶部。当参考组由党外成员组成时，政治上不一致的项目会上升到顶部。虽然这有助于让读者接触到新信息，但他们也无法阅读和分享政治新闻。总体而言，我们的研究对使用社交媒体作为政治信息的出口如何影响甚至极化我们的政治偏好具有重要意义。他们也不再阅读和分享政治新闻。总体而言，我们的研究对使用社交媒体作为政治信息的出口如何影响甚至极化我们的政治偏好具有重要意义。他们也不再阅读和分享政治新闻。总体而言，我们的研究对使用社交媒体作为政治信息的出口如何影响甚至极化我们的政治偏好具有重要意义。