G. Cormack, Email Spam Filtering: A Systematic Review, Foundations and Trends?? in Information Retrieval, vol.1, issue.4, pp.335-455, 2006.
DOI : 10.1561/1500000006

. Nexgate, 2013 State of Social Media SpamNexgate-2013-State-of-Social-Media-Spam-Research-Report, 2013.

V. Prieto, M. Alvarez, and F. Cacheda, Detecting linkedin spammers and its spam nets, International Journal of Advanced Computer Science and Applications (IJACSA), vol.4, issue.9, pp.189-199, 2013.

A. Barushka, P. Hajek, G. Adorni, S. Cagnoni, M. Gori et al., Spam Filtering Using Regularized Neural Networks with Rectified Linear Units, Lecture Notes in Computer Science, vol.20, issue.3, pp.65-75, 2016.
DOI : 10.1007/978-3-319-44188-7_5

A. Bhowmick and S. Hazarika, E-Mail Spam Filtering: A Review of Techniques and Trends, Lecture Notes in Electrical Engineering, vol.177, issue.10, pp.583-590, 2018.
DOI : 10.1016/j.ins.2006.12.005

T. Almeida, J. Almeida, and A. Yamakami, Spam filtering: how the dimensionality reduction affects the??accuracy of Naive Bayes classifiers, Journal of Internet Services and Applications, vol.8, issue.3, pp.183-200, 2011.
DOI : 10.1016/S0019-9958(65)90241-X
URL : https://link.springer.com/content/pdf/10.1007%2Fs13174-010-0014-7.pdf

N. Choudhary and A. Jain, Towards Filtering of SMS Spam Messages Using Machine Learning Based Technique, Communications in Computer and Information Science, vol.27, issue.6, pp.18-30, 2017.
DOI : 10.1145/1321440.1321486

P. Kaur, A. Singhal, and . Kaur, Spam detection on Twitter: A survey, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp.2570-2573, 2016.

. Statista, https://www.statista.com/statistics/264810/number-of-monthly-active-facebook- users-worldwide

X. Zheng, Z. Zeng, Z. Chen, Y. Yu, and C. Rong, Detecting spammers on social networks, Neurocomputing, vol.159, pp.27-34, 2015.
DOI : 10.1016/j.neucom.2015.02.047

A. Wang, Don't follow me: Spam detection in Twitter, Proceedings of the 2010 International Conference on Security and Cryptography (SECRYPT), IEEE, pp.1-10, 2010.

F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida, Detecting spammers on twitter, 6 th Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (CEAS), pp.1-12, 2010.

R. Aswani, A. Kar, and P. Ilavarasan, Detection of spammers in twitter marketing: a hybrid approach using social media analytics and bio inspired computing Information Systems Frontiers 1?16, 2017.

H. Shen, F. Ma, X. Zhang, L. Zong, X. Liu et al., Discovering social spammers from multiple views, Neurocomputing, vol.225, pp.49-57, 2017.
DOI : 10.1016/j.neucom.2016.11.013

A. Gogoglou, Z. Theodosiou, T. Kounoudes, A. Vakali, and Y. Manolopoulos, Early malicious activity discovery in microblogs by social bridges detection, 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp.2016-132, 2016.
DOI : 10.1109/ISSPIT.2016.7886022

P. Bindu, R. Mishra, and P. Thilagam, Discovering spammer communities in twitter, Journal of Intelligent Information Systems, vol.159, issue.8, 2018.
DOI : 10.1016/j.neucom.2015.02.047

F. Wu, J. Shu, Y. Huang, and Z. Yuan, Co-detecting social spammers and spam messages in microblogging via exploiting social contexts, Neurocomputing, vol.201, pp.51-65, 2016.
DOI : 10.1016/j.neucom.2016.03.036

X. Zheng, X. Zhang, Y. Yu, T. Kechadi, and C. Rong, ELM-based spammer detection in social networks, The Journal of Supercomputing, vol.120, issue.10, pp.2991-3005, 2016.
DOI : 10.1016/j.neucom.2013.04.017

D. Yu, N. Chen, F. Jiang, B. Fu, and A. Qin, Constrained NMF-based semi-supervised learning for social media spammer detection, Knowledge-Based Systems, vol.125, pp.64-73, 2017.
DOI : 10.1016/j.knosys.2017.03.025

M. Bosma, E. Meij, and W. Weerkamp, A Framework for Unsupervised Spam Detection in Social Networking Sites, European Conference on Information Retrieval, pp.364-375, 2012.
DOI : 10.1007/978-3-642-28997-2_31
URL : http://ilps.science.uva.nl/sites/ilps.science.uva.nl/files/ecir2012-spam.pdf

A. Barushka and P. Hajek, Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks, Applied Intelligence, vol.40, issue.3, 2018.
DOI : 10.1007/s10462-011-9285-z

S. Dutta, S. Ghatak, R. Dey, A. Das, and S. Ghosh, Attribute selection for improving spam classification in online social networks: a rough set theory-based approach, Social Network Analysis and Mining, vol.5, issue.2, pp.1-16, 2018.
DOI : 10.1109/NAFIPS.2004.1336322

M. Sohrabi and F. Karimi, A Feature Selection Approach to Detect Spam in the Facebook Social Network, Arabian Journal for Science and Engineering, vol.38, issue.1, pp.949-958, 2018.
DOI : 10.1145/2897845.2897928

L. Song, R. Lau, R. Kwok, K. Mirkovski, and W. Dou, Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection, Electronic Commerce Research, vol.97, issue.4, pp.51-81, 2017.
DOI : 10.2307/2342192

G. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors, 2012.

I. Dhillon, S. Mallela, and R. Kumar, A divisive information-theoretic feature clustering algorithm for text classification, Journal of Machine Learning Research, vol.3, pp.1265-1287, 2003.

A. Maas, A. Hannun, and A. Ng, Rectifier nonlinearities improve neural network acoustic models, Proceedings of the 30th International Conference on Machine Learning, pp.1-6, 2013.

Y. Freund and R. Schapire, Experiments with a new boosting algorithm, Thirteenth International Conference on Machine Learning, pp.148-156, 1996.

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.2307/1403680
URL : https://link.springer.com/content/pdf/10.1007%2FBF00058655.pdf

T. Ho, The random subspace method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.2034, issue.8, pp.832-844, 1998.