Profiling Online Social Networks for Spam Detection

Authors(2) :-K. Srinivasan, V. Sureka

Social network has become a very general way for internet users to connect and interact online. Users spend sufficiently of time on famous social networks (e.g., Facebook, Twitter, Sina Weibo, etc.), reading news, discussing events and posting messages. Unfortunately, this popularity also attracts a significant amount of spammers who continuously expose malicious behaviour (e.g., post messages containing commercial URLs, following a larger amount of users, etc.), foremost to great misinterpretation and inconvenience on users' social activities. In this paper, a supervised machine learning based solution is proposed for an effective spam detection.

Authors and Affiliations

K. Srinivasan
M.Phil Research Scholar, Department of Computer Science, Sri Jayendra Saraswathy Maha Vidyalaya CAS, Coimbatore, Tamil Nadu, India
V. Sureka
Assistant Professor, Department of Computer Science, Sri Jayendra Saraswathy Maha Vidyalaya CAS, Coimbatore, Tamil Nadu, India

OSN, k-NN classifier, Spam detection

  1. Mccord, M., & Chuah, M. (2011, September). Spam detection on twitter using traditional classifiers. In international conference on Autonomic and trusted computing (pp. 175-186). Springer, Berlin, Heidelberg.
  2. McMinn, A. J., Moshfeghi, Y., & Jose, J. M. (2013, October). Building a large-scale corpus for evaluating event detection on twitter. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management (pp. 409-418). ACM.
  3. Song, J., Lee, S., & Kim, J. (2011). Spam filtering in twitter using sender-receiver relationship. In Recent advances in intrusion detection (pp. 301-317). Springer Berlin/Heidelberg.
  4. Yardi, S., Romero, D. Detecting spam in a twitter network. First Monday 15(1), pp. 7-14, 2010.
  5. Zi Chu, Steven Gianvecchio, Haining Wang and Sushil Jajodia. Who is Tweeting on Twitter: Human, Bot, or Cyborg? In ACSAC, pp. 21-30, 2010.
  6. F. Benevenuto, T. Rodrigues, V. Almeida, J. Almeida, and M. Gonalves. Detecting Spammers and Content Promoters in Online Video Social Networks. In SIGIR, pp. 620-627, 2009.
  7. H. Gao, J. Hu, C. Wilson, Z. Li, Y. Chen, and B. Zhao. Detecting and Characterizing Social Spam Campaigns. In IMC, pp. 35-47, 2010.
  8. Thomas, K., Grier, C., Song, D., & Paxson, V. (2011, November). Suspended accounts in retrospect: an analysis of twitter spam. In Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference (pp. 243-258). ACM.
  9. Agarwal, S., & Sureka, A. (2015, February). Using knn and svm based one-class classifier for detecting online radicalization on twitter. In International Conference on Distributed Computing and Internet Technology (pp. 431-442). Springer, Cham.
  10. Galán-García, P., Puerta, J. G. D. L., Gómez, C. L., Santos, I., & Bringas, P. G. (2016). Supervised machine learning for the detection of troll profiles in twitter social network: Application to a real case of cyberbullying. Logic Journal of the IGPL, 24(1), 42-53.
  11. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.

Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 488-491
Manuscript Number : CSEIT1725107
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

K. Srinivasan, V. Sureka, "Profiling Online Social Networks for Spam Detection", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.488-491, September-October-2017.
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