Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach

Abstract : As online social networking sites become more and more popular, they have also attracted the attentions of the spammers. In this paper, Twitter, a popular micro-blogging service, is studied as an example of spam bots detection in online social networking sites. A machine learning approach is proposed to distinguish the spam bots from normal ones. To facilitate the spam bots detection, three graph-based features, such as the number of friends and the number of followers, are extracted to explore the unique follower and friend relationships among users on Twitter. Three content-based features are also extracted from user's most recent 20 tweets. A real data set is collected from Twitter's public available information using two different methods. Evaluation experiments show that the detection system is efficient and accurate to identify spam bots in Twitter.
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Communication dans un congrès
Sara Foresti; Sushil Jajodia. 24th Annual IFIP WG 11.3 Working Conference on Data and Applications Security and Privacy (DBSEC), Jun 2010, Rome, Italy. Springer, Lecture Notes in Computer Science, LNCS-6166, pp.335-342, 2010, Data and Applications Security and Privacy XXIV. 〈10.1007/978-3-642-13739-6_25〉
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Alex Hai Wang. Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach. Sara Foresti; Sushil Jajodia. 24th Annual IFIP WG 11.3 Working Conference on Data and Applications Security and Privacy (DBSEC), Jun 2010, Rome, Italy. Springer, Lecture Notes in Computer Science, LNCS-6166, pp.335-342, 2010, Data and Applications Security and Privacy XXIV. 〈10.1007/978-3-642-13739-6_25〉. 〈hal-01056675〉

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