Abstract
Micro-blogging, which has greatly influenced people’s life, is experiencing fantastic success in the worldwide. However, during its rapid development, it has encountered the problem of content pollution. Various pollution in the micro-blogging platforms has hurt the credibility of micro-blogging and caused significantly negative effect. In this paper, we mainly focus on detecting fake followers which may lead to a problematic situation on social media networks. By extracting major features of fake followers in Sina Weibo, we propose a binary classifier to distinguish fake followers from the legitimate users. The experiments show that all the proposed features are important and our method greatly outperforms to detect fake followers. We also present an elaborate analysis on the phenomenon of fake followers, infer the supported algorithms and principles behind them, and finally provide several suggestions for micro-blogging systems and ordinary users to deal with the fake followers.
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References
Stringhini, G., Kruegel, C., Vigna, G.: Detecting spammers on social networks. In: ACSAC 2010 Conference Proceedings, pp. 1–9 (2010)
Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: WWW 2010 Conference Proceedings, pp. 591–600 (2010)
Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: EMNLP 2011 Conference Proceedings, pp. 1589–1599 (2011)
Mendoza, M., Poblete, B., Castillo, C.: Twitter under crisis: Can we trust what we rt? In: Proceedings of the First Workshop on Social Media Analytics, pp. 71–79 (2010)
Grier, C., Thomas, K., Paxson, V., Zhang, M.: @spam: the underground on 140 characters or less. In: CCS 2010 Conference Proceedings, pp. 27–37 (2010)
McCord, M., Chuah, M.: Spam detection on twitter using traditional classifiers. In: Calero, J.M.A., Yang, L.T., Mármol, F.G., García Villalba, L.J., Li, A.X., Wang, Y. (eds.) ATC 2011. LNCS, vol. 6906, pp. 175–186. Springer, Heidelberg (2011)
Egele, M., Stringhini, G., Kruegel, C., Vigna, G.: Compa: Detecting compromised accounts on social networks. In: Symposium on Network and Distributed System Security, NDSS (2013)
Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B.Y.: Detecting and characterizing social spam campaigns. In: IMC 2010 Conference Proceedings, pp. 35–47 (2010)
Stringhini, G., Egele, M., Kruegel, C., Vigna, G.: Poultry markets: On the underground economy of twitter followers. In: WOSN 2012 Conference Proceedings, pp. 1–6 (2012)
Ghosh, S., Viswanath, B., Kooti, F., Sharma, N.K., Korlam, G., Benevenuto, F., Ganguly, N., Gummadi, K.P.: Understanding and combating link farming in the twitter social network. In: WWW 2012 Conference Proceedings, pp. 61–70 (2012)
Thomas, K., Grier, C., Song, D., Paxson, V.: Suspended accounts in retrospect: an analysis of twitter spam. In: IMC 2011 Conference Proceedings, pp. 243–258 (2011)
Yang, C., Harkreader, R., Zhang, J., Shin, S., Gu, G.: Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. In: WWW 2012 Conference Proceedings, pp. 71–80 (2012)
Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on twitter. In: Collaboration, Electronic Messaging, Anti-abuse and Spam Conference (CEAS), vol. 6 (2010)
Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers: social honeypots+ machine learning. In: SIGIR 2010 Conference Proceedings, pp. 435–442 (2010)
Zhu, Y., Wang, X., Zhong, E., Liu, N.N., Li, H., Yang, Q.: Discovering spammers in social networks. In: AAAI 2012 Conference Proceedings, pp. 171–177 (2012)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: The million follower fallacy. ICWSM 10, 10–17 (2010)
Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. In: UAI 2004 Conference Proceedings, pp. 487–494 (2004)
Chang, J., Gerrish, S., Wang, C., Boyd-graber, J.L., Blei, D.M.: Reading tea leaves: How humans interpret topic models. In: Advances in Neural Information Processing Systems, pp. 288–296 (2009)
Kwak, H., Chun, H., Moon, S.: Fragile online relationship: a first look at unfollow dynamics in twitter. In: CHI 2011 Conference Proceedings, pp. 1091–1100 (2011)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2, 1–27 (2011)
Quinlan, J.R.: Bagging, boosting, and c4. 5. In: AAAI/IAAI, vol. 1, pp. 725–730 (1996)
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Shen, Y., Yu, J., Dong, K., Nan, K. (2014). Automatic Fake Followers Detection in Chinese Micro-blogging System. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_49
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DOI: https://doi.org/10.1007/978-3-319-06605-9_49
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06604-2
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