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Improved Tweet Bot Detection Using Spatio-Temporal Information
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 Title & Authors
Improved Tweet Bot Detection Using Spatio-Temporal Information
Kim, Hyo-Sang; Shin, Won-Yong; Kim, Donggeon; Cho, Jaehee;
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 Abstract
Twitter, one of online social network services, is one of the most popular micro-blogs, which generates a large number of automated programs, known as tweet bots because of the open structure of Twitter. While these tweet bots are categorized to legitimate bots and malicious bots, it is important to detect tweet bots since malicious bots spread spam and malicious contents to human users. In the conventional work, temporal information was utilized for the classficiation of human and bot. In this paper, by utilizing geo-tagged tweets that provide high-precision location information of users, we first identify both Twitter users` exact location and the corresponding timestamp, and then propose an improved two-stage tweet bot detection algorithm by computing an entropy based on spatio-temporal information. As a main result, the proposed algorithm shows superior bot detection and false alarm probabilities over the conventional result which only uses temporal information.
 Keywords
Geo-tagged tweet;spatio-temporal information;online social network;Twitter;tweet bot;
 Language
Korean
 Cited by
1.
Twitter turing test: Identifying social machines, Information Sciences, 2016, 372, 332  crossref(new windwow)
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