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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
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Author(s): Arti Jain (Jaypee Institute of Information Technology, India), Reetika Gairola (Jaypee Institute of Information Technology, India), Shikha Jain (Jaypee Institute of Information Technology, India)and Anuja Arora (Jaypee Institute of Information Technology, India)
Copyright: 2018
Pages: 20
Source title: Social Network Analytics for Contemporary Business Organizations
Source Author(s)/Editor(s): Himani Bansal (Jaypee Institute of Information Technology, India), Gulshan Shrivastava (National Institute of Technology Patna, India), Gia Nhu Nguyen (Duy Tan University, Vietnam)and Loredana-Mihaela Stanciu (University Timisoara, Romania)
DOI: 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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