论文标题
2020年美国选举期间网络搜索的个性化
Personalization of Web Search During the 2020 US Elections
论文作者
论文摘要
搜索引擎在将政治信息与公民路线路线路线路线路线路线路线路线路线路线路线路线路线路线路由过程中发挥作用。像Google一样，大型搜索引擎对搜索结果的算法个性化表示，可能会系统地提供不同的用户。但是，在政治上相关的环境中衡量用户特征和行为对搜索结果的因果效应具有挑战性。我们建立了150个合成互联网用户（“ bot”）的人口，他们位于25个美国城市中，并在2020年美国选举中活跃了几个月。这些用户在浏览偏好和政治意识形态方面有所不同，并建立了现实的浏览和搜索历史。我们进行每日实验，所有用户都输入相同的与选举相关的查询。这些查询的搜索结果在用户之间有很大的不同。 Google优先考虑先前访问的网站和本地新闻网站。但是，它通常不会优先考虑以用户意识形态为特色的网站。
Search engines play a central role in routing political information to citizens. The algorithmic personalization of search results by large search engines like Google implies that different users may be offered systematically different information. However, measuring the causal effect of user characteristics and behavior on search results in a politically relevant context is challenging. We set up a population of 150 synthetic internet users ("bots") who are randomly located across 25 US cities and are active for several months during the 2020 US Elections and their aftermath. These users differ in their browsing preferences and political ideology, and they build up realistic browsing and search histories. We run daily experiments in which all users enter the same election-related queries. Search results to these queries differ substantially across users. Google prioritizes previously visited websites and local news sites. Yet, it does not generally prioritize websites featuring the user's ideology.
