Nguyen Vo
ABSTRACT
Retweeting/sharing action has enabled information to be cascaded to distant nodes on social network. Unfortunately, malicious users as a group have taken advantage of the retweeting function with coordinated behavior to falsely distort the volume of specific keywords, topics or URLs for promotional purposes (e.g., spreading fake news, and increasing public visibility of products or services). Unfortunately, little is known about their retweeting behavior as a group and how to detect them based on group-based signals. To fill the gap, in this paper, we (i) propose Attractor+ algorithm to extract retweeter groups, members of each of which have similar retweeting behavior; (ii) analyze underlying characteristics of malicious and legitimate retweeter groups; (iii) propose group-based features to catch synchronized and coordinated behavior; and build a predictor to classify if a group is malicious. Experimental results show that our proposed method outperformed existing approaches.
- Z. Yang, J. Guo, K. Cai, J. Tang, J. Li, L. Zhang, and Z. Su, "Understanding retweeting behaviors in social networks," in CIKM, 2010.Google Scholar
- Sysomos, "https://sysomos.com/inside-twitter/twitter-retweet-stats," in Replies and Retweets on Twitter, 2010.Google Scholar
- B. Liu, J. Luo, J. Cao, X. Ni, B. Liu, and X. Fu, "On crowd-retweeting spamming campaign in social networks," in ICC, 2016.Google Scholar
- Z. Qunyan, Z. Chi, C. Peng, Q. Weining, and Z. Aoying, "Detecting spamming groups in social media based on latent graph," in ADC, 2015.Google Scholar
- N. Chavoshi, H. Hamooni, and A. Mueen, "Debot: Twitter bot detection via warped correlation," in ICDM, 2016.Google Scholar
- C. Cao, J. Caverlee, K. Lee, H. Ge, and J. Chung, "Organic or organized? exploring url sharing behavior," in CIKM, 2015.Google Scholar
- G. Maria, C. Despoina, S. Neil, B. Alex, F. Christos, and V. Athena, "Nd-sync: Detecting synchronized fraud activities," in PAKDD, 2015.Google Scholar
- K. Lee, J. Caverlee, Z. Cheng, and D. Z. Sui, "Campaign extraction from social media," ACM Trans. Intell. Syst. Technol., 2014.Google Scholar
- L. K. G. Rumi, S. Tawan, "Entropy-based classification of 'retweeting'activity on twitter," arXiv:1106.0346, 2011.Google Scholar
- A. Ferraz Costa, Y. Yamaguchi, A. Juci Machado Traina, C. Traina Jr, and C. Faloutsos, "Rsc: Mining and modeling temporal activity in social media," in KDD. ACM, 2015. Google ScholarDigital Library
- Boyd, Danah, S. Golder, and G. Lotan, "Tweet, tweet, retweet: Conversational aspects of retweeting on twitter," in HICSS, 2010.Google Scholar
- N. Naveed, T. Gottron, J. Kunegis, and A. C. Alhadi, "Bad news travel fast: A content-based analysis of interestingness on twitter," in WebSci, 2011.Google Scholar
- S. Petrovi, "Rt to win! predicting message propagation in twitter," in ICWSM, 2011.Google Scholar
- B. Suh, L. Hong, P. Pirolli, and E. H. Chi, "Want to be retweeted? large scale analytics on factors impacting retweet in twitter network," in SocialCom, 2010.Google Scholar
- L. Hong, O. Dan, and B. D. Davison, "Predicting popular messages in twitter," in International World Wide Web Conference, 2011. Google ScholarDigital Library
- K. Lee, J. Mahmud, J. Chen, M. Zhou, and J. Nichols, "Who will retweet this? automatically identifying and engaging strangers on twitter to spread information," in IUI, 2014.Google Scholar
- H. Gao, J. Hu, C. Wilson, Z. Li, Y. Chen, and B. Y. Zhao, "Detecting and characterizing social spam campaigns," in SIGCOMM. ACM, 2010.Google Scholar
- E. Ferrara, O. Varol, C. Davis, F. Menczer, and A. Flammini, "The rise of social bots," arXiv preprint arXiv:1407.5225, 2014.Google Scholar
- V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, "Fast unfolding of communities in large networks," Journal of Statistical Mechanics: Theory and Experiment, 2008. Google ScholarCross Ref
- J. Shao, Z. Han, Q. Yang, and T. Zhou, "Community detection based on distance dynamics," in KDD, 2015.Google Scholar
- S. Fortunato, "Community detection in graphs," Physics reports, vol. 486, no. 3, pp. 75--174, 2010. Google ScholarCross Ref
- R. Zafarani and H. Liu, "Connecting users across social media sites: a behavioral-modeling approach," in KDD, 2013.Google Scholar
- D. V. Huntsberger, Elements of statistical inference. Allyn & Bacon, Inc., 1986.Google Scholar
- J. R. Landis and G. G. Koch, "The measurement of observer agreement for categorical data," biometrics, 1977.Google Scholar
- K. Lee, B. D. Eoff, and J. Caverlee, "Seven months with the devils: A long-term study of content polluters on twitter," in ICWSM, 2011.Google Scholar
- Revealing and Detecting Malicious Retweeter Groups
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