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

  • Nan Jing
  • Zhao WuEmail author
  • Shanshan Lyu
  • Vijayan Sugumaran
Article
  • 27 Downloads

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.

Keywords

Professional social networks Information credibility Trustworthiness probability Tree augmented naïve Bayes classifier PageRank algorithm 

Notes

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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information Management, SHU-UTS SILC Business SchoolShanghai UniversityShanghaiPeople’s Republic of China
  2. 2.Department of Decision and Information Sciences, School of Business AdministrationOakland UniversityRochesterUSA
  3. 3.Center for Data Science and Big Data AnalyticsOakland UniversityRochesterUSA

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