Title: The Role of Follow Networks and Twitter's Content Recommender on Partisan Skew and Rumor Exposure during the 2022 U.S. Midterm Election Show Chinese title

Title: 跟进网络和推特内容推荐器在2022年美国中期选举期间党派偏见和谣言曝光中的作用

Abstract: Social media platforms shape users' experiences through the algorithmic systems they deploy. In this study, we examine to what extent Twitter's content recommender, in conjunction with a user's social network, impacts the topic, political skew, and reliability of information served on the platform during a high-stakes election. We utilize automated accounts to document Twitter's algorithmically curated and reverse chronological timelines throughout the U.S. 2022 midterm election. We find that the algorithmic timeline measurably influences exposure to election content, partisan skew, and the prevalence of low-quality information and election rumors. Critically, these impacts are mediated by the partisan makeup of one's personal social network, which often exerts greater influence than the algorithm alone. We find that the algorithmic feed decreases the proportion of election content shown to left-leaning accounts, and that it skews content toward right-leaning sources when compared to the reverse chronological feed. We additionally find evidence that the algorithmic system increases the prevalence of election-related rumors for right-leaning accounts, and has mixed effects on the prevalence of low-quality information sources. Our work provides insight into the outcomes of Twitter's complex recommender system at a crucial time period before controversial changes to the platform and in the midst of nationwide elections and highlights the need for ongoing study of algorithmic systems and their role in democratic processes. Show Chinese abstract

Abstract: 社交媒体平台通过其部署的算法系统塑造用户的经验。 在本研究中，我们探讨了Twitter的内容推荐系统与用户的社交网络结合，如何影响高风险选举期间平台上提供的主题、政治倾向和信息的可靠性。 我们利用自动账户记录美国2022年中期选举期间Twitter的算法策划和倒序时间线。 我们发现，算法时间线显著影响了选举内容的曝光度、党派偏见以及低质量信息和选举谣言的普遍性。 关键的是，这些影响受到个人社交网络的党派构成的调节，这通常比算法本身的影响更大。 我们发现，算法信息流减少了向左翼账户显示的选举内容比例，并且与倒序时间流相比，使内容偏向右翼来源。 我们还发现证据表明，算法系统增加了右翼账户中与选举相关的谣言的普遍性，并对低质量信息来源的普遍性产生了混合影响。 我们的工作提供了关于Twitter复杂推荐系统在平台发生有争议的变化之前这一关键时刻的成果的见解，并在全国范围内选举进行的同时，强调了对算法系统及其在民主进程中的作用进行持续研究的必要性。