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ENWalk: Learning Network Features for Spam Detection in Twitter

  • K. C. SantoshEmail author
  • Suman Kalyan Maity
  • Arjun Mukherjee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

Social medias are increasing their influence with the vast public information leading to their active use for marketing by the companies and organizations. Such marketing promotions are difficult to identify unlike the traditional medias like TV and newspaper. So, it is very much important to identify the promoters in the social media. Although, there are active ongoing researches, existing approaches are far from solving the problem. To identify such imposters, it is very much important to understand their strategies of social circle creation and dynamics of content posting. Are there any specific spammer types? How successful are each types? We analyze these questions in the light of social relationships in Twitter. Our analyses discover two types of spammers and their relationships with the dynamics of content posts. Our results discover novel dynamics of spamming which are intuitive and arguable. We propose ENWalk, a framework to detect the spammers by learning the feature representations of the users in the social media. We learn the feature representations using the random walks biased on the spam dynamics. Experimental results on large-scale twitter network and the corresponding tweets show the effectiveness of our approach that outperforms the existing approaches.

Keywords

Social network Spam detection Feature learning 

Notes

Acknowledgements

This work is supported in part by NSF 1527364. We also thank anonymous reviewers for their helpful feedbacks.

References

  1. 1.
    Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on twitter. In: Collaboration, Electronic Messaging, Anti-abuse and Spam Conference (CEAS), vol. 6, p. 12 (2010)Google Scholar
  2. 2.
    Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., Ghosh, R.: Exploiting burstiness in reviews for review spammer detection. In: Proceedings of the Seventh International Conference on Weblogs and Social Media, ICWSM 2013, Cambridge, Massachusetts, USA, 8–11 July 2013Google Scholar
  3. 3.
    Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B.Y.: Detecting and characterizing social spam campaigns. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp. 35–47 (2010)Google Scholar
  4. 4.
    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: Proceedings of the 21st International Conference on World Wide Web, pp. 61–70 (2012)Google Scholar
  5. 5.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)Google Scholar
  6. 6.
    Hu, X., Tang, J., Zhang, Y., Liu, H.: Social spammer detection in microblogging. IJCAI 2013, 2633–2639 (2013)Google Scholar
  7. 7.
    K C, S., Mukherjee, A.: On the temporal dynamics of opinion spamming: case studies on yelp. In: 25th International World Wide Web Conference, WWW 2016, Montréal, Québec, Canada, 11–15 April 2016Google Scholar
  8. 8.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: The International World Wide Web Conference Committee (IW3C2), pp. 1–10 (2010)Google Scholar
  9. 9.
    Lee, K., 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. 435–442 (2010)Google Scholar
  10. 10.
    Li, H., Mukherjee, A., Liu, B., Kornfield, R., Emery, S.: Detecting campaign promoters on twitter using markov random fields. In: 2014 IEEE International Conference on Data Mining, ICDM, Shenzhen, China, pp. 290–299, 14–17 December 2014Google Scholar
  11. 11.
    Mikolov, T., Chen, K., Corrado, G. and Dean, J.: Distributed representations of words and phrases and their compositionality. Nips, pp. 1–9 (2013)Google Scholar
  12. 12.
    Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the International Conference on Learning Representations (ICLR 2013), pp. 1–12 (2013)Google Scholar
  13. 13.
    Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)Google Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)Google Scholar
  16. 16.
    Thomas, K., Grier, C., Song, D., Paxson, V.: Suspended accounts in retrospect: an analysis of twitter spam. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 243–258 (2011)Google Scholar
  17. 17.
    Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining (WSDM 2010), pp. 261–270 (2010)Google Scholar
  18. 18.
    Yang, J., Leskovec, J.: Patterns of temporal variation in online media. WSDM 2011, 177 (2011)Google Scholar
  19. 19.
    Zhang, X., Zhu, S., Liang, W.: Detecting spam and promoting campaigns in the Twitter social network. In: Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 1194–1199 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • K. C. Santosh
    • 1
    Email author
  • Suman Kalyan Maity
    • 2
  • Arjun Mukherjee
    • 1
  1. 1.University of HoustonHoustonUSA
  2. 2.IIT KharagpurKharagpurIndia

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