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Social networks provide users with a way to stay in
touch with friends. Increasing the popularity of social networks allows all of
them to collect a large amount of personal information about their users.
Unfortunately, the wealth of this information and the convenience of accessing
user information can be of concern to malicious groups. That's why these
networks are being invaded by spammers, and there is a lot of work to diagnose
and fix them. Regarding this issue, spammers are looking for new ways to locate
these networks every day, so they are constantly taking action to identify
spammers and malicious emails. The purpose of this article is to study previous
work in the field of spam detection in social networks along with the brief
introduction about social media as well as their related techniques used for
the detection of spam.
. Jin, X., Lin, C. X., Luo, J., & Han, J. (2011, August). Socialspamguard: A data mining-based spam detection system for social media networks. In Proceedings of the international conference on very large data bases.
. Jain, G., Sharma, M., & Agarwal, B. (2018). Spam detection on social media using semantic convolutional neural network. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 12-26.
. Ferrara, E. (2018). Measuring social spam and the effect of bots on information diffusion in social media. In Complex Spreading Phenomena in Social Systems (pp. 229-255). Springer, Cham.
. Aswani, R., Kar, A. K., & Ilavarasan, P. V. (2018). Detection of spammers in twitter marketing: a hybrid approach using social media analytics and bio inspired computing. Information Systems Frontiers, 1-16.
. Jindal, N., & Liu, B. (2007, May). Review spam detection. In Proceedings of the 16th international conference on World Wide Web (pp. 1189-1190). ACM.
. Xie, S., Wang, G., Lin, S., & Yu, P. S. (2012, August). Review spam detection via temporal pattern discovery. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 823-831). ACM.
. Crawford, M., Khoshgoftaar, T. M., Prusa, J. D., Richter, A. N., & Al Najada, H. (2015). Survey of review spam detection using machine learning techniques. Journal of Big Data, 2(1), 23.
. Heydari, A., ali Tavakoli, M., Salim, N., & Heydari, Z. (2015). Detection of review spam: A survey. Expert Systems with Applications, 42(7), 3634-3642.
. Xie, S., Wang, G., Lin, S., & Yu, P. S. (2012, April). Review spam detection via time series pattern discovery. In Proceedings of the 21st International Conference on World Wide Web (pp. 635-636). ACM.
. Wang, G., Xie, S., Liu, B., & Philip, S. Y. (2011, December). Review graph based online store review spammer detection. In 2011 IEEE 11th International Conference on Data Mining(pp. 1242-1247). IEEE.
. Alex hai way, Webb, S., & Pu, C. (2010, May). Study of static classification of social spam profiles in myspace. In Fourth International AAAI Conference on Weblogs and Social Media,
. Gianluca Stringhini M., Pan, Y., & Yuan, B. (2010, November). A social approach to security: Using social networks to help detect malicious web content. In 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering (pp. 436-441). IEEE.
. Saeed Abu Nimeh, D., & El Abbadi, A. (2011). Information diffusion in social networks: Observing and influencing societal interests. PVLDB, 4(12)
. De wang Webb et al. (2008). Increasing the veracity of event detection on social media networks through user trust modeling.
. Dwyer, C., Hiltz, S., & Passerini, K. (2007). Trust and privacy concern within social networking sites: A comparison of Facebook and MySpace. AMCIS 2007 proceedings, 339.
. Jin, X., Lin, C., Luo, J., & Han, J. (2011). A data mining-based spam detection system for social media networks. Proceedings of the VLDB Endowment, 4(12), 1458-1461.