Abstract
Given the re-broadcasts (i.e. retweets) of posts in Twitter, how can we spot fake from genuine user reactions? What will be the tell-tale sign — the connectivity of retweeters, their relative timing, or something else? High retweet activity indicates influential users, and can be monetized. Hence, there are strong incentives for fraudulent users to artificially boost their retweets’ volume. Here, we explore the identification of fraudulent and genuine retweet threads. Our main contributions are: (a) the discovery of patterns that fraudulent activity seems to follow (the “triangles ” and “homogeneity ” patterns, the formation of micro-clusters in appropriate feature spaces); and (b) “RTGen ”, a realistic generator that mimics the behaviors of both honest and fraudulent users. We present experiments on a dataset of more than 6 million retweets crawled from Twitter.
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References
Beutel, A., et al.: CopyCatch: stopping group attacks by spotting lockstep behavior in social networks. In: WWW, pp. 119–130. ACM (2013)
Chu, Z., et al.: Who is Tweeting on Twitter: Human, Bot, or Cyborg? ACSAC, 21–30 (2010)
Derrida, B., et al.: Statistical Properties of Randomly Broken Objects and of Multivalley Structures in Disordered Systems. Journal of Physics A: Mathematical and General 20(15), 5273–5288 (1987)
Erdos, P., et al.: On the evolution of Random Graphs. Publ. Math. Inst. Hungary. Acad. Sci. 5, 17–61 (1960)
Ghosh, R., et al.: Entropy-based classification of ‘retweeting’ activity on twitter. In: KDD Workshop on Social Network Analysis (SNA-KDD) (2011)
Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: Inferring strange behavior from connectivity pattern in social networks. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014, Part I. LNCS, vol. 8443, pp. 126–138. Springer, Heidelberg (2014)
Kempe, D., et al.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 137–146. ACM, New York (2003)
Kurt, T., et al.: Suspended Accounts in Retrospect: an Analysis of Twitter Spam. IMC, 243–258 (2011)
Kwak, H., et al.: What is Twitter, a Social Network or a News Media? In: WWW, pp. 591–600 (2010)
Leskovec, J., et al.: Kronecker Graphs: An Approach to Modeling Networks. JMLR 11, 985–1042 (2010)
Lin, P.-C., et al.: A Study of Effective Features for Detecting Long-surviving Twitter Spam Accounts. ICACT 841 (2013)
Mao, H.-H., Wu, C.-J., Papalexakis, E.E., Faloutsos, C., Lee, K.-C., Kao, T.-C.: MalSpot: Multi\(^\text{2 }\) malicious network behavior patterns analysis. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014, Part I. LNCS, vol. 8443, pp. 1–14. Springer, Heidelberg (2014)
Pandit, S., et al.: Netprobe: a fast and scalable system for fraud detection in online auction networks. In: WWW, pp. 201–210. ACM (2007)
Rao, A., et al.: Modeling and Analysis of Real World Networks using Kronecker Graphs. Project report (2010)
Schroeder, M.: Fractals, Chaos, Power Laws, 6th edn. W. H. Freeman, New York (1991)
Tavares, G., et al.: Scaling-Laws of Human Broadcast Communication Enable Distinction between Human, Corporate and Robot Twitter Users. PLoS ONE 8(7), e65774 (2013)
Wu, X., Feng, Z., Fan, W., Gao, J., Yu, Y.: Detecting marionette microblog users for improved information credibility. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part III. LNCS, vol. 8190, pp. 483–498. Springer, Heidelberg (2013)
Yang, C., et al.: Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. In: WWW, pp. 71–80 (2012)
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Giatsoglou, M., Chatzakou, D., Shah, N., Faloutsos, C., Vakali, A. (2015). Retweeting Activity on Twitter: Signs of Deception. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9077. Springer, Cham. https://doi.org/10.1007/978-3-319-18038-0_10
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