Segregating Spammers and Unsolicited Bloggers from Genuine Experts on Twitter

Online Social Networks (OSNs) have not only significantly reformed the social interaction pattern but have also emerged as an effective platform for recommendation of services and products. The upswing in use of the OSNs has also witnessed growth in unwanted activities on social media. On the one hand, the spammers on social media can be a high risk towards the security of legitimate users and on the other hand some of the legitimate users, such as bloggers can pollute the results of recommendation systems that work alongside the OSNs. The polluted results of recommendation systems can be precarious to the masses that track recommendations. Therefore, it is necessary to segregate such type of users from the genuine experts. We propose a framework that separates the spammers and unsolicited bloggers from the genuine experts of a specific domain. The proposed approach employs modified Hyperlink Induced Topic Search (HITS) to separate the unsolicited bloggers from the experts on Twitter on the basis of tweets. The approach considers domain specific keywords in the tweets and several tweet characteristics to identify the unsolicited bloggers. Experimental results demonstrate the effectiveness of the proposed methodology as compared to several state-of-the-art approaches and classifiers.