Abstract
The growing use of the smart device such as smartphones and tablets has resulted in increasing number of social network service (SNS) users recently. SNS allows a fast propagation and it is used as a tool to send information. But its negative sides need to be considered. In this paper, we analyzed actual data of malicious accounts and extracted features. Based on this results, we detect the suspected accounts that spread rumors. Firstly, we crawled actual data and analyzed feature. And we selected feature as three approaches and added a new feature as propagation approach by existing work. That is user can re-tweet influencer’s tweet and edit it. We discussed it by ratio for RT. After that, we selected classification standard using average of data based on selected feature and trained it. Bayesian network is used for training. And the system may provide a new classification through re-analysis of the data. Proposed system is that the accuracy is 91.94 % and F-measure is 93.76 %.
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Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on Twitter. In: Collaboration, Electronic Messaging, Anti-abuse and Spam Conference (CEAS), vol. 6, pp. 12 (2010)
Ellison, N.B., Steinfield, C., Lampe, C.: The benefits of Facebook friends: social capital and college students use of online social network sites. J. Comput. Mediated Commun. 12(4), 1143–1168 (2007)
Gao, H., Chen, Y., Lee, K., Palsetia, D., Choudhary, A.N.: Towards online spam filtering in social networks. In: Proceedings of 19th Network Distributed System Security Symposium, vol. 29, No. 23, pp. 1–10 (2010)
Gurajala, S., White, J.S., Hudson, B., Matthews, J.N.: Fake Twitter accounts: profile characteristics obtained using an activity-based pattern detection approach. In: Proceedings of the 2015 International Conference on Social Media and Society, p. 9. ACM (2015)
Jenson, F.V.: An introduction to Bayesian networks, vol. 210. UCL Press, London (1996)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media. In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600 (2010)
Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: IEEE 13th International Conference on Data Mining (ICDM), pp. 1103–1108 (2013)
Kyumin, L., Caverlee, J., Webb, S.: Uncovering social spammers: social honeypots + machine learning. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1139–1140. ACM (2010)
Bosma, M., Meij, E., Weerkamp, W.: A framework for unsupervised spam detection in social networking sites. In: Baeza-Yates, R., de Vries, A.P., Zaragoza, H., Cambazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 364–375. Springer, Heidelberg (2012)
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)
Qazvinian, V., Radev, E., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: Proceeding of the Conference on Empirical Methods in Natural Languate Processing (EMNLP), pp. 1589–1599 (2011)
Starbird, K., Maddock, J., Orand, M., Achterman, P., Mason, R.M.: Rumors, false flags, and digital vigilantes: misinformation on Twitter after the 2013 Boston Marathon Bombing. In: iConference on 2014 Proceedings (2014)
Stringhini, G., Kruegel, C., Vigna, G.: Detecting spammers on social networks. In: Proceedings of the 26th Annual Computer Security Applications Conference, pp. 1–9 (2010)
Viswanath, B., Post, A., Gummadi, K.P.: An analysis of social network-based Sybil defenses. ACM SIGCOMM Comput. Commun. Rev. 41(4), 363–374 (2011)
Vosoughi, S.: Automatic detection and verification of rumors on Twitter. Diss. Massachusetts Institute of Technology (2015)
Wang, A.H.: Don’t follow me: spam detection in Twitter. In: Proceedings of the 2010 International Conference on Security and Cryptography (SECRYPT), vol. 29, no. 23, pp. 1–10 (2010)
Wu, K., Yang, S., Zhu, H.Q.: False rumors detection on Sina Weibo by propagation structures. In: IEEE International Conference on Data Engineering, ICDE, pp. 651–662 (2015)
Zhu, Y., Wang, X., Zhong, E., Liu, N.N., Li, H., Yang, Q.: Discovering spammers in social networks. In: 26th AAAI Conference on Artificial Intelligence (2012)
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Yang, H., Zhong, J., Ha, D., Oh, H. (2016). Rumor Propagation Detection System in Social Network Services. In: Nguyen, H., Snasel, V. (eds) Computational Social Networks. CSoNet 2016. Lecture Notes in Computer Science(), vol 9795. Springer, Cham. https://doi.org/10.1007/978-3-319-42345-6_8
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DOI: https://doi.org/10.1007/978-3-319-42345-6_8
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