ACM Transactions on Internet Technology (TOIT)
Link spam detection based on mass estimation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Transductive link spam detection
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Know your neighbors: web spam detection using the web topology
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Proceedings of the first workshop on Online social networks
Link based small sample learning for web spam detection
Proceedings of the 18th international conference on World wide web
Detecting spammers and content promoters in online video social networks
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Social linking and physical proximity in a mobile location-based service
Proceedings of the 1st international workshop on Mobile location-based service
Identifying automatic posting systems in microblogs
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Detecting Credential Abuse in the Grid Using Bayesian Networks
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
How much money do spammers make from your website?
Proceedings of the CUBE International Information Technology Conference
REPLOT: REtrieving profile links on Twitter for suspicious networks detection
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Twitter spammer detection using data stream clustering
Information Sciences: an International Journal
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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.