Information credibility evaluation in online professional social network using tree augmented naïve Bayes classifier

In recent years, companies depend on the Internet for posting job advertisements and attracting qualified personnel. However, with the vast number of Internet users and the tremendous amount of information on the Internet, it is difficult to accurately evaluate the credibility of the information that candidates provide on the Internet. Therefore, we propose an approach to assess information credibility in terms of trustworthiness and authority to identify unreliable user profiles in online professional social networks. Our approach calculates the trustworthiness probabilities of user profile information using the Tree Augmented Naïve Bayes (TAN) classifier. It also measures the authority of individual users by applying the PageRank algorithm for analyzing the user interactions in the professional social networks. Finally, a group of LinkedIn users’ profiles is selected for conducting experiments to validate the proposed approach. Experiments based on a real-world scenario show that our approach integrating the TAN Bayes and PageRank algorithm outperforms other existing approaches in classification accuracy. In addition, the approach has been applied to another social network, namely, Maimai in China to further demonstrate its usefulness.