Cross-Domain Spam Detection in Social Media: A Survey

  • Deepali DhakaEmail author
  • Monica Mehrotra
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 985)


Social media is now an integral part of everyone’s life. Due to its exponential growth with rising interest of users, they have become the source of the abundant amount of data prevailing on the internet. This tremendous amount of data is not only useful for researchers but also fascinates spammers. To reach more users, to increase monetary gain and to disseminate malicious activities, spammers are using multiple content sharing platforms. Conventional spam detection techniques have focused more on spam detection on one platform only. This paper discusses cross-domain detection techniques of email and web spams, social spams, opinion spams, and their comparisons. This is an attempt to provide various challenges in this area. As far as our knowledge concerned, this is the first detailed literature study in the field of cross-domain spam detection.


Cross-domain Content-based Spam Social media Spams detection 


  1. 1.
    Grier, C., Thomas, K., Paxson, V., Zhang, M.: @ spam: the underground on 140 characters or less. In: Proceedings of the 17th ACM Conference on Computer and Communications Security, pp. 27–37. ACM (2010)Google Scholar
  2. 2.
    Mathur, A., Prachi, G.: Spam detection techniques: issues and challenges. Int. J. Appl. Inf. Syst. (IJAIS) (2013). ISSN 2249-0868Google Scholar
  3. 3.
    Kaur, R., Singh, S., Kumar, H.: Rise of spam and compromised accounts in online social networks: a state-of-the-art review of different combating approaches. J. Netw. Comput. Appl. 112, 53–88 (2018)CrossRefGoogle Scholar
  4. 4.
    Sun, Q., et al.: Transfer learning for bilingual content classification. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2147–2156. ACM (2015)Google Scholar
  5. 5.
    Ramalingam, D., Chinnaiah, V.: Fake profile detection techniques in large-scale online social networks: a comprehensive review. Comput. Electr. Eng. 65, 165–177 (2018)CrossRefGoogle Scholar
  6. 6.
    Bruns, A., et al.: Detecting Twitter bots that share SoundCloud tracks. In: Proceedings of the International Conference on Social Media + Society, vol. 8, pp. 251–255. ACM Press (2018)Google Scholar
  7. 7.
    Thomas, K., Grier, C., Ma, J., Paxson, V., Song, D.: Design and evaluation of a real-time URL spam filtering service. In: 2011 IEEE Symposium on Security and Privacy (SP), pp. 447–462. IEEE (2011)Google Scholar
  8. 8.
    Stringhini, G., Kruegel, C., Vigna, G.: Detecting spammers on social networks. In: Proceedings of the 26th Annual Computer Security Applications Conference, pp. 1–9. ACM (2010)Google Scholar
  9. 9.
    Webb, S., Caverlee, J., Pu, C.: Introducing the web spam corpus: using email spam to identify web spam automatically. In: Proceedings of the Third Conference on Email and Anti-Spam (CEAS) (2006)Google Scholar
  10. 10.
    Wang, D., Irani, D., Pu, C.: A social-spam detection framework. In: Proceedings of the 8th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, pp. 46–54. ACM (2011)Google Scholar
  11. 11.
    Wang, D., Irani, D., Pu, C.: Spade: a social-spam analytics and detection framework. Soc. Netw. Anal. Min. 4(1), 189 (2014)CrossRefGoogle Scholar
  12. 12.
    Lumezanu, C., Feamster, N.: Observing common spam in Twitter and email. In: Proceedings of the 2012 Internet Measurement Conference, pp. 461–466. ACM (2012)Google Scholar
  13. 13.
    Ahmed, F., Abulaish, M.: A generic statistical approach for spam detection in Online Social Networks. Comput. Commun. 36(10–11), 1120–1129 (2013)CrossRefGoogle Scholar
  14. 14.
    Hu, X., Tang, J., Liu, H.: Leveraging knowledge across media for spammer detection in microblogging. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 547–556. ACM (2014)Google Scholar
  15. 15.
    Yang, X., Zhang, T., Xu, C.: Cross-domain feature learning in multimedia. IEEE Trans. Multimed. 17(1), 64–78 (2015)CrossRefGoogle Scholar
  16. 16.
    Xu, H., Sun, W., Javaid, A.: Efficient spam detection across online social networks. In: 2016 IEEE International Conference on Big Data Analysis (ICBDA), pp. 1–6. IEEE (2016)Google Scholar
  17. 17.
    Li, J., Ott, M., Cardie, C., Hovy, E.: Towards a general rule for identifying deceptive opinion spam. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1566–1576 (2014)Google Scholar
  18. 18.
    Hernández-Castañeda, Á., Calvo, H., Gelbukh, A., Flores, J.J.G.: Cross-domain deception detection using support vector networks. Soft. Comput. 21(3), 585–595 (2017)CrossRefGoogle Scholar
  19. 19.
    Li, L., Qin, B., Ren, W., Liu, T.: Document representation and feature combination for deceptive spam review detection. Neurocomputing 254, 33–41 (2017)CrossRefGoogle Scholar
  20. 20.
    Ren, Y., Ji, D.: Neural networks for deceptive opinion spam detection: an empirical study. Inf. Sci. 385, 213–224 (2017)CrossRefGoogle Scholar
  21. 21.
    Hai, Z., Zhao, P., Cheng, P., Yang, P., Li, X.L., Li, G.: Deceptive review spam detection via exploiting task relatedness and unlabeled data. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1817–1826 (2016)Google Scholar
  22. 22.
    Henke, M., Souto, E., dos Santos, E.M.: Analysis of the evolution of features in classification problems with concept drift: application to spam detection. In: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 874–877. IEEE (2015)Google Scholar
  23. 23.
    Zheng, X., Zhang, X., Yu, Y., Kechadi, T., Rong, C.: ELM-based spammer detection in social networks. J. Supercomputing 72(8), 2991–3005 (2015)CrossRefGoogle Scholar
  24. 24.
    Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 219–230. ACM (2008)Google Scholar
  25. 25.
    Jain, G., Sharma, M., Agarwal, B.: Spam detection in social media using convolutional and long short term memory neural network. Ann. Math. Artif. Intell. 85, 21–44 (2019). Scholar
  26. 26.
    Jain, G., Sharma, M., Agarwal, B.: Spam detection on social media using semantic convolutional neural network. Int. J. Knowl. Discov. Bioinf. (IJKDB) 8(1), 12–26 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceJamia Millia IslamiaNew DelhiIndia

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