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

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Abstract

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.

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Acknowledgements

This work has been sponsored by the Ministry of Education of the People’s Republic of China (Grant No. 2016YB138), the Science and Technology Commission Shanghai Municipality (Grant No. 18692106500), and the Shanghai Municipal Education Commission.

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Authors and Affiliations

  1. Department of Information Management, SHU-UTS SILC Business School, Shanghai University, Shanghai, 201800, People’s Republic of China

    Nan Jing, Zhao Wu & Shanshan Lyu

  2. Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, MI, 48309, USA

    Vijayan Sugumaran

  3. Center for Data Science and Big Data Analytics, Oakland University, Rochester, MI, 48309, USA

    Vijayan Sugumaran

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  1. Nan Jing
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  2. Zhao Wu
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Correspondence to Zhao Wu.

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Jing, N., Wu, Z., Lyu, S. et al. Information credibility evaluation in online professional social network using tree augmented naïve Bayes classifier. Electron Commer Res 21, 645–669 (2021). https://doi.org/10.1007/s10660-019-09387-y

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