TY - GEN
T1 - Uncovering fake likers in online social networks
AU - Satya, Prudhvi Ratna Badri
AU - Lee, Kyumin
AU - Lee, Dongwon
AU - Tran, Thanh
AU - Zhang, Jason
N1 - Funding Information:
This work was supported in part by NSF grants CNS-1422215, CNS-1553035, and IUSE-1525601. Any opinions, findings and conclusions or recommendations expressed in this material are the author(s) and do not necessarily reflect those of the sponsors.
Publisher Copyright:
© 2016 ACM.
PY - 2016/10/24
Y1 - 2016/10/24
N2 - As the commercial implications of Likes in online social networks multiply, the number of fake Likes also increase rapidly. To maintain a healthy ecosystem, however, it is critically important to prevent and detect such fake Likes. Toward this goal, in this paper, we investigate the problem of detecting the so-called "fake likers" who frequently make fake Likes for illegitimate reasons. To uncover fake Likes in online social networks, we: (1) first collect a substantial number of profiles of both fake and legitimate Likers using linkage and honeypot approaches, (2) analyze the characteristics of both types of Likers, (3) identify effective features exploiting the learned characteristics and apply them in supervised learning models, and (4) thoroughly evaluate their performances against three baseline methods and under two attack models. Our experimental results show that our proposed methods with effective features significantly outperformed baseline methods, with accuracy = 0.871, false positive rate = 0.1, and false negative rate = 0.14.
AB - As the commercial implications of Likes in online social networks multiply, the number of fake Likes also increase rapidly. To maintain a healthy ecosystem, however, it is critically important to prevent and detect such fake Likes. Toward this goal, in this paper, we investigate the problem of detecting the so-called "fake likers" who frequently make fake Likes for illegitimate reasons. To uncover fake Likes in online social networks, we: (1) first collect a substantial number of profiles of both fake and legitimate Likers using linkage and honeypot approaches, (2) analyze the characteristics of both types of Likers, (3) identify effective features exploiting the learned characteristics and apply them in supervised learning models, and (4) thoroughly evaluate their performances against three baseline methods and under two attack models. Our experimental results show that our proposed methods with effective features significantly outperformed baseline methods, with accuracy = 0.871, false positive rate = 0.1, and false negative rate = 0.14.
UR - http://www.scopus.com/inward/record.url?scp=84996539988&partnerID=8YFLogxK
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U2 - 10.1145/2983323.2983695
DO - 10.1145/2983323.2983695
M3 - Conference contribution
AN - SCOPUS:84996539988
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2365
EP - 2370
BT - CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Y2 - 24 October 2016 through 28 October 2016
ER -