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Improved Tweet Bot Detection Using Geo-Location and Device Information

지리적 공간과 장치 정보를 사용한 개선된 트윗 봇 검출

Lee, Al-Chan;Seo, Go-Eun;Shin, Won-Yong;Kim, Donggeon;Cho, Jaehee
이알찬;서고은;신원용;김동건;조재희

  • Received : 2015.08.10
  • Accepted : 2015.09.14
  • Published : 2015.12.31

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. Then, we propose a new tweet bot detection algorithm by using both an entropy based on geographic variable of each user and device information of each user. 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

Device information;geographic information;geo-tagged tweet;online social network;Twitter;tweet bot

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Acknowledgement

Supported by : National Research Foundation of Korea (NRF), Ministry of Science, ICT & Future Planning (MSIP)