[1] Takahashi T, Tomioka R, Yamanishi K. Discovering emerging topics in social streams via link-anomaly detection. IEEE Trans. Knowledge and Data Engineering, 2014, 26(1):120-130.[2] Guille A, Favre C. Mention-anomaly-based event detection and tracking in Twitter. In Proc. the IEEE International Conference on Advances in Social Network Analysis and Mining, August 2014, pp.375-382[3] Savage D, Zhang X, Yu X et al. Anomaly detection in online social networks. Social Networks, 2014, 39:62-70.[4] O'Callaghan D, Harrigan M, Carthy J et al. Network analysis of recurring YouTube spam campaigns. In Proc. the 6th AAAI Conference on Weblogs and Social Media, June 2012, pp.531-534.[5] Gao H, Hu J, Huang T et al. Security issues in online social networks. IEEE Internet Computing, 2011, 15(4):56-63.[6] Zhu Y, Wang X, Zhong E et al. Discovering spammers in social networks. In Proc. the 26th AAAI Conference on Artificial Intelligence, July 2012, pp.171-177.[7] Kwak H, Lee C, Park H et al. What is Twitter, a social network or a news media? In Proc. the 19th WWW, April 2010, pp.591-600.[8] Wu S, Hofman J M, Mason W A et al. Who says what to whom on Twitter. In Proc. the 20th WWW, Match 28-April 1, 2011, pp.705-714.[9] Yu L, Asur S, Huberman B A. What trends in Chinese social media. In Proc. the 5th SNA-KDD Workshop, August 2011.[10] Gao Q, Abel F, Houben G et al. A comparative study of users' microblogging behavior on Sina Weibo and Twitter. In Lecture Notes in Computer Science 7379, Masthoff J, Mobasher B, Desmarais M C et al. (eds.), Springer Berlin Heidelberg, 2012, pp.88-101.[11] McCord M, Chuah M. Spam detection on Twitter using traditional classifiers. In Lecture Notes in Computer Science 6906, Alcaraz Calero J M, Yang L T, Mármol F G et al. (eds.), Springer Berlin Heidelberg, 2011, pp.175-186.[12] Martinez-Romo J, Araujo L. Detecting malicious tweets in trending topics using a statistical analysis of language. Expert Systems with Applications:An International Journal, 2013, 40(8):2992-3000.[13] Bosma M, Meij E, Weerkamp W. A framework for unsupervised spam detection in social networking sites. In Lecture Notes in Computer Science 7224, Baeza-Yates R, de Vries A P, Zaragoza H et al. (eds.), Springer Berlin Heidelberg, 2012, pp.364-375.[14] Altshuler Y, Fire M, Shmueli E et al. Detecting anomalous behaviors using structural properties of social networks. In Proc. the 6th International Conference on Social Computing, Behavioral Cultural Modeling and Prediction, April 2013, pp.433-440.[15] Zhang Q, Ma H, QianW et al. Duplicate detection for identifying social spam in microblogs. In Proc. the 2nd IEEE International Congress on Big Data, June 27-July 2, 2013, pp.141-148.[16] Chu Z, Widjaja I, Wang H. Detecting social spam campaigns on Twitter. In Lecture Notes in Computer Science 7341, Bao F, Samarati P, Zhou J (eds.), Springer Berlin Heidelberg, 2012, pp.455-472.[17] Jiang J, Wilson C, Wang X et al. Understanding latent interactions in online social networks. In Proc. the 10th ACM SIGCOMM Conference on Internet Measurement, November 2010, pp.369-382.[18] Chen Y, Wang L, Dong M. Non-negative matrix factorization for semi-supervised heterogeneous data coclustering. IEEE Trans. Knowledge and Data Engineering, 2010, 22(10):1459-1474.[19] Tang L,Wang X F, Liu H. Community detection via heterogeneous interaction analysis. Data Mining and Knowledge Discovery, 2012, 25(1):1-33.[20] Hu X, Tang J L, Zhang Y C et al. Social spammer detection in microblogging. In Proc. the 23rd International Joint Conference on Artificial Intelligence, August 2013, pp.2633-2639.[21] Hu X, Tang J L, Liu H. Online social spammer detection. In Proc. the 28th AAAI Conference on Artificial Intelligence, July 2014, pp.59-65.[22] Dai H, Zhu F, Lim E et al. Detecting anomalies in bipartite graphs with mutual dependency principles. In Proc. the 12th ICDM, December 2012, pp.171-180.[23] Sun J, Qu H, Chakrabarti D et al. Neighborhood formation and anomaly detection in bipartite graphs. In Proc. the 5th ICDM, Nov. 2005, pp.418-425.[24] Akoglu L, Tong H, Koutra D. Graph based anomaly detection and description:A survey. Data Mining and Knowledge Discovery, 2014, 29(3):626-688.[25] Zhao B, Ji G, QuW et al. Detecting spam community using retweeting relationships-A study on Sina microblog. In Lecture Notes in Computer Science 8178, Cao L, Motoda H, Srivastava J et al. (eds.), Springer International Publishing, 2013, pp.178-190.[26] Bhat S Y, Abulaish M. Community-based features for identifying spammers in online social networks. In Proc. the 2013 IEEE International Conference on Advances in Social Networks Analysis and Mining, August 2013, pp.100-107.[27] Yu R, He X R, Liu Y. GLAD:Group anomaly detection in social media analysis. In Proc. the 20th ACM SIGKDD KDD, August 2014, pp.372-381.[28] Xing E P, Ng A Y, Jordan M I et al. Distance metric learning, with application to clustering with side-information. In Proc. the 16th Neural Information Processing Systems, December 2002, pp.505-512.[29] Wang H, Nie F P, Huang H. Robust distance metric learning via simultaneous l1-norm minimization and maximization. In Proc. the 31st International Conference on Machine Learning, June 2014, pp.1836-1844.[30] Chang C C, Lin C J. LIBSVM:A library for support vector machines. ACM Trans. Intelligent Systems and Technology, 2011, 2(3):27:1-27:27.[31] Hu X, Tang J L, Liu H. Leveraging knowledge across media for spammer detection in microblogging. In Proc. the 37th SIGIR, July 2014, pp.547-556.[32] Hu X, Tang J L, Gao H J et al. Social spammer detection with sentiment information. In Proc. the 14th ICDM, December 2014, pp.180-189. |