A Study of Spam Detection Algorithm on Social Media Networks

  • Jacob Soman Saini
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 246)


In the present situation, the issue of identifying spammers has received increasing attention because of its practical relevance in the field of social network analysis. The growing popularity of social networking sites has made them prime targets for spammers. By allowing users to publicize and share their independently generated content, online social networks become susceptible to different types of malicious and opportunistic user actions. Social network community users are fed with irrelevant information while surfing, due to spammer’s activity. Spam pervades any information system such as email or Web, social, blog, or reviews platform. Therefore, this paper attempts to review various spam detection frameworks that which deal about the detection and elimination of spams in various sources.


Spam detection Spam analysis Feature extraction 


  1. 1.
    Danah michele boyd, “Friendster and publicly articulated social networking”, proceedings of Conference on Human Factors and Computing Systems (CHI 2004), pp. 1279–1282, 2004.Google Scholar
  2. 2.
    Markus Jakobsson, Jacob Ratkiewicz, “Designing ethical phishing experiments: a study of (ROT13) rOnl query features”, Proceedings of the 15th international conference on World Wide Web (2006), pp. 513–522, 2006.Google Scholar
  3. 3.
    Alex Tsow and Markus Jakobsson, Deceit and Deception: A Large User Study of Phishing, Technical Report TR649, Indiana University, Bloomington, August 2007.Google Scholar
  4. 4.
    Takeda, T.; Takasu, A., “A splog filtering method based on string copy detection”, proceedings of First International Conference on Applications of Digital Information and Web Technologies, pp. 543–548, 2008.Google Scholar
  5. 5.
    Kamaliha, E.; Riahi, F.; Qazvinian, V.; Adibi, J., “Characterizing Network Motifs to Identify Spam Comments”, proceedings of IEEE International Conference on Data Mining Workshops, pp. 919–928, 2008.Google Scholar
  6. 6.
    Webb, Steve, Caverlee, James and Pu, Calton, Social Honeypots: “Making Friends With A Spammer Near You”, Paper presented at the meeting of the CEAS, 2008.Google Scholar
  7. 7.
    Gianluca Stringhini, Christopher Kruegel, Giovanni Vigna, “Detecting Spammers on Social Networks”, proceedings of Annual Computer Security Applications Conference (ACSAC) 2010.Google Scholar
  8. 8.
    Kyumin Lee, James Caverlee, Steve Webb, “Uncovering Social Spammers: Social Honeypots +Machine Learning”, proceedings of ACM-SIGIR 2010.Google Scholar
  9. 9.
    Sreenivasan, Shrijina, Lakshmipathi, B., “An Unsupervised Model to detect Web Spam based on Qualified Link Analysis and Language Models”, International Journal of Computer Applications, vol. 63, issue 4, pp. 33–37, 2013.Google Scholar
  10. 10.
    K. Karthick, V. Sathiya, J. Pugalendiran, “Detecting Nepotistic Links Based On Qualified Link Analysis and Language Models”, International Journal of Computer Trends and Technology, pp. 106–109, June 2011.Google Scholar
  11. 11.
    Qureshi, M.A.; Younus, A.; Touheed, N.; Qureshi, M.S.; Saeed, M., “Discovering Irrelevance in the Blogosphere through Blog Search”, proceedings of International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 457–460, 2011.Google Scholar
  12. 12.
    Jenq-Haur Wang; Ming-Sheng Lin, “Using Inter-comment Similarity for Comment Spam Detection in Chinese Blogs”, proceedings of International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 189–194, 2011.Google Scholar
  13. 13.
    Archana Bhattarai, Vasile Rus, and Dipankar Dasgupta, “Characterizing Comment Spam in the Blogosphere through Content Analysis”, proceedings of IEEE Symposium on Computational Intelligence in Cyber Security—CICS, pp. 37–44, 2009.Google Scholar
  14. 14.
    Sakakura, Y.; Amagasa, T.; Kitagawa, H., “Detecting Social Bookmark Spams Using Multiple User Accounts”, proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1153–1158, 2012.Google Scholar
  15. 15.
    Yang Yu; Yuzhong Chen, “A novel content based and social network aided online spam short message filter”, proceedings of 10th World Congress on Intelligent Control and Automation (WCICA), pp. 444–449, 2012.Google Scholar
  16. 16.
    Ravindran, P.P.; Mishra, A.; Kesavan, P.; Mohanavalli, S., “Randomized tag recommendation in social networks and classification of spam posts”, proceedings of IEEE International Workshop on Business Applications of Social Network Analysis (BASNA), pp. 1–6, 2010.Google Scholar
  17. 17.
    Ariaeinejad, R.; Sadeghian, A., “Spam detection system: A new approach based on interval type-2 fuzzy sets”, proceedings of 24th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 379–384, 2011.Google Scholar
  18. 18.
    Ishida, K., “Mutual detection between spam blogs and keywords based on cooccurrence cluster seed”, proceedings of First International Conference on Networked Digital Technologies, pp. 8–13, 2009.Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSree Narayana Gurukulam College of EngineeringKadayiruppu, KolencheryIndia

Personalised recommendations