2022/03/31 by Hassan Iqbal, Usman Mahmood Khan, Hassan Ali Khan +1 · 3 voices · 4 citations
Computer Science · Social Sciences · #Algorithm #Computer science #Filter (signal processing) #Internet Traffic Analysis and Secure E-voting #Internet privacy #Law #Political science #Politics #Presidential system #Privacy, Security, and Data Protection #Spam and Phishing Detection #World Wide Web #cs.CY
paper · pdf · doi:10.1145/3485447.3512121
published in Proceedings of the ACM Web Conference 2022, 2491-2500 · 10 pages, Published in WWW'22
arxiv created 2022/03/31 · arxiv updated 2022/04/01 · openalex publication_date 2022/04/25 · openalex created_date 2022/04/26 · openalex updated_date 2026/08/05
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.