Abstract

The messaging systems and social media is popular and has essential contributions to our social and professional life. Similarly, Spam is a part of the messaging system and social media. In social media, spam is found in various places (i.e. in posts, in comments, in reviews, and in chatting). Social media Spam is aimed to influence the user’s decision, point of view, and credibility of the service or brand. Therefore, social spam detection is essential. However, using the social media data a number of contributions are available in literature, but a fewer amount of work is available for social media spam detection. In this paper, we proposed a social media spam detection technique using machine learning and text feature extraction techniques. In this context first, a review on social media spam detection techniques has been carried out. Using this review, we extract the different machine learning techniques used, techniques of text feature selection, and experimental datasets used. In this review, we found that the spam messages with the URLs are more critical and harmful. Next step, we design a theoretical model for social media spam detection, which includes text feature selection techniques (i.e. TF-IDF, POS, and Information Gain) and their combinations (POS+TF-IDF and POS+IG). These features are used with Support Vector Machine (SVM), Artificial Neural Network, and Naïve Bayes classifier for training. Experimental analysis with dataset available in Kaggle we found that hybrid features is more effective for accurate classification as compared to individual features. Additionally, we found for classification the SVM and ANN are more accurate as compared to the Bayes classifier.