The presence of political misinformation and ideological echo chambers on social media platforms is concerning given the important role that these sites play in the public's exposure to news and current events. Algorithmic systems employed on these platforms are presumed to play a role in these phenomena, but little is known about their mechanisms and effects. In this work, we conduct an algorithmic audit of Twitter's Who-To-Follow friend recommendation system, the first empirical audit that investigates the impact of this algorithm in-situ. We create automated Twitter accounts that initially follow left and right affiliated U.S. politicians during the 2022 U.S. midterm elections and then grow their information networks using the platform's recommender system. We pair the experiment with an observational study of Twitter users who already follow the same politicians. Broadly, we find that while following the recommendation algorithm leads accounts into dense and reciprocal neighborhoods that structurally resemble echo chambers, the recommender also results in less political homogeneity of a user's network compared to accounts growing their networks through social endorsement. Furthermore, accounts that exclusively followed users recommended by the algorithm had fewer opportunities to encounter content centered on false or misleading election narratives compared to choosing friends based on social endorsement.

翻译：政治虚假信息与意识形态回音室在社交媒体平台上的存在令人担忧，鉴于这些网站在公众接触新闻和时事方面所扮演的重要角色。这些平台采用的算法系统被认为在这些现象中发挥作用，但对其机制和影响知之甚少。在本研究中，我们对Twitter的"关注谁"好友推荐系统进行了算法审计，这是首个实证审计，旨在调查该算法在原位环境下的影响。我们创建了自动Twitter账户，这些账户在2022年美国中期选举期间最初关注左派和右派关联的美国政治家，然后利用平台的推荐系统扩展其信息网络。我们将实验与对已关注相同政治家的Twitter用户的观察研究相结合。总体而言，我们发现虽然遵循推荐算法会使账户进入结构上类似回音室的密集互惠社区，但与通过社交背书扩展网络的账户相比，推荐系统实际上使用户网络的同质性降低。此外，与基于社交背书选择好友相比，仅关注算法推荐用户的账户接触以虚假或误导性选举叙事为核心内容的机会更少。