论文标题
在2020年美国选举期间，在电子邮件垃圾邮件过滤算法中窥视政治偏见
A Peek into the Political Biases in Email Spam Filtering Algorithms During US Election 2020
论文作者
论文摘要
电子邮件服务使用垃圾邮件过滤算法（SFA）来过滤用户不需要的电子邮件。但是，有时，SFA认为不需要的电子邮件对用户可能很重要。如果SFA将大规模的垃圾邮件视为用户兴趣的电子邮件，则此类错误的决定可能会产生重大影响。这在全国选举中尤其重要。为了研究流行电子邮件服务的SFA是否有任何偏见来治疗广告系列电子邮件，我们通过订阅了大量总统，参议院和众议院候选人，对Gmail，Outlook和Yahoo进行了一百多个电子邮件帐户，对2020年美国选举的竞选电子邮件进行了大规模研究。我们分析了SFA对左侧和右候选人的偏见，并进一步研究了电子邮件收件人对这些偏见的互动（例如阅读或标记电子邮件）的影响。我们观察到，不同电子邮件服务的SFA确实对不同的政治隶属关系表现出偏见。我们在本文中介绍了这一点和其他一些重要观察。
Email services use spam filtering algorithms (SFAs) to filter emails that are unwanted by the user. However, at times, the emails perceived by an SFA as unwanted may be important to the user. Such incorrect decisions can have significant implications if SFAs treat emails of user interest as spam on a large scale. This is particularly important during national elections. To study whether the SFAs of popular email services have any biases in treating the campaign emails, we conducted a large-scale study of the campaign emails of the US elections 2020 by subscribing to a large number of Presidential, Senate, and House candidates using over a hundred email accounts on Gmail, Outlook, and Yahoo. We analyzed the biases in the SFAs towards the left and the right candidates and further studied the impact of the interactions (such as reading or marking emails as spam) of email recipients on these biases. We observed that the SFAs of different email services indeed exhibit biases towards different political affiliations. We present this and several other important observations in this paper.
