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http://dx.doi.org/10.6109/jkiice.2015.19.12.2878

Improved Tweet Bot Detection Using Geo-Location and Device Information  

Lee, Al-Chan (Department of Mobile Systems Engineering, Dankook University)
Seo, Go-Eun (Department of Mobile Systems Engineering, Dankook University)
Shin, Won-Yong (Department of Computer Science and Engineering, Dankook University)
Kim, Donggeon (Department of Statistics and Information Science, Dongduk Women's University)
Cho, Jaehee (Department of Management, Kwangwoon University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.19, no.12, 2015 , pp. 2878-2884 More about this Journal
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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1 M. C. Gonzalez, C. A. Hidalgo, and A. L. Batabasi, "Understanding individual human mobility patterns," Nature, vol. 453, pp. 591-600, Apr. 2010.
2 D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A.-L. Barabasi, "Human mobility, social ties, and link prediction," in Proceedings of the 17th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD2011), San Diego, CA USA, pp.1100-1108, Aug. 2011.
3 B. Hawelka, I. Sitko, E. Beinat, S. Sobolevsky, P. Kazakopoulos, and C. Ratti, "Geo-located Twitter as proxy for global mobility patterns," Cartography and Geographic Information Science, vol. 41, no. 3, pp. 260-271, May 2014.   DOI
4 R. Jurdak, K. Zhao, J. Liu, M. AbouJaoude, M. Cameron, and D. Newth, "Understanding human mobility from Twitter," PLOS ONE, vol. 10, no. 7, pp. 1-16, July 2015.
5 W.-Y. Shin, B. C. Singh, J. Cho, and A. M. Everett, "A new understanding of friendships in space: Complex networks meet Twitter," Journal of Information Science, vol. 41, no. 6, pp. 751-564, Dec. 2015.   DOI
6 S. Y. Jeon, A. C. Lee, G. E. Seo, and W. Y. Shin, "Relationship between tweet frequency and user velocity on Twitter," Journal of the Korea Institute of Information and Communication Engineering, vol. 19, no. 6, pp. 1380-1386, Jun. 2015.   DOI
7 Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia, "Detecting automation of Twitter accounts: Are you a human, bot, or cyborg?," IEEE Transactions on Dependable and Secure Computing, vol. 9, no.6, pp. 811-824, Dec. 2012.   DOI
8 C. Wilson, B. Boe, A.Sala, K. P. N. Puttaswamy, and B. Y. Zhao, "User interaction in social networks and their implication," in Proceedings of the 4th ACM European Conference on Computer Systems (EuroSys '09), Nuremberg, Germany, pp. 205-218, Mar./Apr. 2009.
9 H. Kwak, C. Lee, H. Park, and S. Moon, "What is Twitter, a social network or a news media?," in Proceedings of the 19th International World Wide Web Conference (WWW2010), Raleigh, NC USA, pp. 591-600, Apr. 2010.