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Thwarting Spam on Facebook: Identifying Spam Posts Using Machine Learning Techniques

Thwarting Spam on Facebook: Identifying Spam Posts Using Machine Learning Techniques

Arti Jain, Reetika Gairola, Shikha Jain, Anuja Arora
Copyright: © 2018 |Pages: 20
ISBN13: 9781522550976|ISBN10: 1522550976|EISBN13: 9781522550983
DOI: 10.4018/978-1-5225-5097-6.ch004
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MLA

Jain, Arti, et al. "Thwarting Spam on Facebook: Identifying Spam Posts Using Machine Learning Techniques." Social Network Analytics for Contemporary Business Organizations, edited by Himani Bansal, et al., IGI Global, 2018, pp. 51-70. https://doi.org/10.4018/978-1-5225-5097-6.ch004

APA

Jain, A., Gairola, R., Jain, S., & Arora, A. (2018). Thwarting Spam on Facebook: Identifying Spam Posts Using Machine Learning Techniques. In H. Bansal, G. Shrivastava, G. Nguyen, & L. Stanciu (Eds.), Social Network Analytics for Contemporary Business Organizations (pp. 51-70). IGI Global. https://doi.org/10.4018/978-1-5225-5097-6.ch004

Chicago

Jain, Arti, et al. "Thwarting Spam on Facebook: Identifying Spam Posts Using Machine Learning Techniques." In Social Network Analytics for Contemporary Business Organizations, edited by Himani Bansal, et al., 51-70. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5097-6.ch004

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Abstract

Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of these networks. Spammers often use certain techniques to deceive the OSN users for their own benefit. Facebook, one of the leading OSNs, is experiencing such crucial problems at an alarming rate. This chapter presents a methodology to segregate spam from legitimate posts using machine learning techniques: naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The textual, image, and video features are used together, which wasn't considered by the earlier researchers. Then, 1.5 million posts and comments are extracted from archival and real-time Facebook data, which is then pre-processed using RStudio. A total of 30 features are identified, out of which 10 are the best informative for identification of spam vs. ham posts. The entire dataset is shuffled and divided into three ratios, out of which 80:20 ratio of training and testing dataset provides the best result. Also, RF classifier outperforms NB and SVM by achieving overall F-measure 89.4% on the combined feature set.

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