Abstract
Spam is an enemy of our resources and mood. In this paper spam detection is examined in case of social media content. In particular a typical Bayesian classifier is setup and trained on real data. Additionally a social computing method is also introduced, which analyses the attention that content receives. Finally a novel hybrid approach is implemented, where content is characterized as spam when both the Bayesian classifier and the social attention model agree on content’s evaluation. Experimental results on real world Twitter content, exhibit the promising performance of the proposed scheme.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Crawford, M., Khoshgoftar, T.D., Prusa, J.N., Ritcher, A., Al Najada, H.: Survey of review spam detection using machine learning techniques. J. Big Data 2, 23(2015)
Shebuti, R., Akoglu, L.: Collective opinion spam detection: bridging review networks and metadata. In: ACM KDD (2015)
Shehnepoor, S., Salehi, M., Farahbakhsh, R., Crespi, N.: NetSpam: a network-based spam detection framework for reviews in online social media. IEEE Trans. Inf. Forensics Secur. 12(7), 1585–1595 (2017)
Cao, C., Caverlee, J.: Detecting spam URLs in social media via behavioral analysis. In: Advances in Information Retrieval, LNCS, vol. 9022. Springer (2015)
Jain, G., Sharma, M., Agarwal, B.: Spam detection on social media using semantic convolutional neural network. Int. J. Knowl. Discov. Bioinf. 8, 12–26 (2018)
Yu, D., Chen, N., Jiang, F., Fu, B., Qin, A.: Knowledge-based systems constrained NMF-based semi-supervised learning for social media spammer detection. Knowl.-Based Syst. 125(1), 64–73 (2017)
Sohrabi, M.K., Karimi, F.: A feature selection approach to detect spam in the Facebook social network. Arab. J. Sci. Eng. 43(2), 949–958 (2018)
Barushka, A., Hajek, P.: Spam filtering in social networks using regularized deep neural networks with ensemble learning. In: Artificial Intelligence Applications and Innovations, IFIP Advances in ICT, vol. 519 (2018)
http://www.mathpages.com/home/kmath267/kmath267.htm. Accessed 2 Aug 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ntalianis, K., Mastorakis, N. (2019). Spam Detection in Social Media: A Bayesian Scheme Based on Social Activity Over Content. In: Ntalianis, K., Vachtsevanos, G., Borne, P., Croitoru, A. (eds) Applied Physics, System Science and Computers III. APSAC 2018. Lecture Notes in Electrical Engineering, vol 574 . Springer, Cham. https://doi.org/10.1007/978-3-030-21507-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-21507-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21506-4
Online ISBN: 978-3-030-21507-1
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)