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

  • Nan Jing
  • Zhao WuEmail author
  • Shanshan Lyu
  • Vijayan Sugumaran


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.


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



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.


  1. 1.
    Fazel-Zarandi, M., & Fox, M. S. (2013). Identifying unreliable sources of skill and competency information. In IEEE/ACM international conference on advances in social networks analysis and mining (pp. 1110–1115). IEEE.Google Scholar
  2. 2.
    Liu, Z. (2004). Perceptions of credibility of scholarly information on the web. Information Processing and Management,40(6), 1027–1038.CrossRefGoogle Scholar
  3. 3.
    Lowry, P. B., Wilson, D. W., & Haig, W. L. (2014). A picture is worth a thousand words: Source credibility theory applied to logo and website design for heightened credibility and consumer trust. International Journal of Human-Computer Interaction,30(1), 63–93.CrossRefGoogle Scholar
  4. 4.
    Li, J. (2015). Advertising value and credibility transfer: Attitude towards web advertising and online information acquisition. Behaviour & Information Technology,34(5), 520–532.CrossRefGoogle Scholar
  5. 5.
    Kato, Y., Inui, K., & Kurohashi, S. (2008). Information credibility on the web. In Internet Research, 18(2).Google Scholar
  6. 6.
    Hajli, N. (2016). Ethical environment in the online communities by information credibility: A social media perspective. Journal of Business Ethics,149(4), 799–810.CrossRefGoogle Scholar
  7. 7.
    Kammerer, Y., & Gerjets, P. (2014). The role of search result position and source trustworthiness in the selection of web search results when using a list or a grid interface. International Journal of Human–Computer Interaction,30(3), 177–191.CrossRefGoogle Scholar
  8. 8.
    Fogg, B. J. (2001). What makes a Web site credible? A report on a large quantitative study. In Acm sigchi conference on human factors in computing systems. ACM Press.Google Scholar
  9. 9.
    Fogg, B. J., & Tseng, H. (1999). The elements of computer credibility. In Proceeding of the CHI ‘99 conference on human factors in computing systems: The CHI is the limit, Pittsburgh, Pa, Usa, May (Vol. 99, pp. 80–87). DBLP.Google Scholar
  10. 10.
    Chung, C. J., Nam, Y., & Stefanone, M. A. (2012). Exploring online news credibility: The relative influence of traditional and technological factors. Journal of Computer-Mediated Communication,17(2), 171–186.CrossRefGoogle Scholar
  11. 11.
    Flanagin, A. J., & Metzger, M. J. (2000). Perceptions of internet information credibility. Journalism & Mass Communication Quarterly,77(3), 515–540.CrossRefGoogle Scholar
  12. 12.
    Wathen, C. N., & Burkell, J. (2014). Believe it or not: Factors influencing credibility on the web. Journal of the Association for Information Science & Technology,53(2), 134–144.Google Scholar
  13. 13.
    Vishwanath, A. (2015). Diffusion of deception in social media: Social contagion effects and its antecedents. Information Systems Frontiers,17(6), 1353–1367.CrossRefGoogle Scholar
  14. 14.
    Cho, J., & Roy, S. (2004). Impact of search engines on page popularity. In International conference on world wide web (pp. 20–29). ACM.Google Scholar
  15. 15.
    Cho, J., Roy, S., & Adams, R. E. (2005). Page quality: In search of an unbiased web ranking. In ACM SIGMOD international conference on management of data (pp. 551–562). ACM.Google Scholar
  16. 16.
    Barnes, J. A. (1954). Class and committees in a Norwegian island parish. Human Relations,7(1), 39–58.CrossRefGoogle Scholar
  17. 17.
    Aylward, B. S., Odar, C. C., Kessler, E. D., Canter, K. S., & Roberts, M. C. (2012). Six degrees of separation: An exploratory network analysis of mentoring relationships in pediatric psychology. Journal of Pediatric Psychology,37(9), 972–979.CrossRefGoogle Scholar
  18. 18.
    Lee, J., Agrawal, M., & Rao, H. R. (2015). Message diffusion through social network service: The case of rumor and non-rumor related tweets during boston bombing 2013. Information Systems Frontiers,17(5), 997–1005.CrossRefGoogle Scholar
  19. 19.
    Fu, Q., Feng, B., Guo, D., & Li, Q. (2018). Combating the evolving spammers in online social networks. Computers & Security,72, 60–73.CrossRefGoogle Scholar
  20. 20.
    Sedhai, S., & Sun, A. (2017). Semi-supervised spam detection in twitter stream. IEEE Transactions on Computational Social Systems,5, 1–7.Google Scholar
  21. 21.
    Chen, C., Zhang, J., Xie, Y., Xiang, Y., Zhou, W., Hassan, M. M., et al. (2015). A performance evaluation of machine learning-based streaming spam tweets detection. IEEE Transactions on Computational Social Systems,2(3), 65–76.CrossRefGoogle Scholar
  22. 22.
    Liang, G., He, W., Xu, C., Chen, L., & Zeng, J. (2015). Rumor identification in microblogging systems based on users’ behavior. IEEE Transactions on Computational Social Systems,2(3), 99–108.CrossRefGoogle Scholar
  23. 23.
    Zucker, L. G. (1986). Production of trust: Institutional sources of economic structure, 1840–1920. In Research in organizational behavior. JAI Press.Google Scholar
  24. 24.
    Al-Oufi, S., Kim, H. N., & El Saddik, A. (2012). A group trust metric for identifying people of trust in online social networks. Expert Systems with Applications,39(18), 13173–13181.CrossRefGoogle Scholar
  25. 25.
    Chen, C. M., Guan, D. J., & Su, Q. K. (2014). Feature set identification for detecting suspicious URLs using bayesian classification in social networks. Information Sciences,289, 133–147.CrossRefGoogle Scholar
  26. 26.
    Bhumiratana, B. (2011). A model for automating persistent identity clone in online social network. In 2011 IEEE 10th international conference on trust, security and privacy in computing and communications. Google Scholar
  27. 27.
    Conti, M., Hasani, A., & Crispo, B. (2013). Virtual private social networks and a facebook implementation. ACM Transactions on the Web,7(3), 1–14.CrossRefGoogle Scholar
  28. 28.
    Pant, G., & Sheng, O. R. L. (2015). Web footprints of firms: Using online isomorphism for competitor identification. Information Systems Research,26(1), 188–209.CrossRefGoogle Scholar
  29. 29.
    Ramalingam, D., & Chinnaiah, V. (2018). Fake profile detection techniques in large-scale online social networks: A comprehensive review. Computers & Electrical Engineering,65, 165–177. Scholar
  30. 30.
    Park, J. -H. (2014). The effects of personalization on user continuance in social networking sites. Information Processing and Management,50(3), 462–475.CrossRefGoogle Scholar
  31. 31.
    Qi, G. J., Aggarwal, C. C., Han, J., & Huang, T. (2013). Mining collective intelligence in diverse groups. In International conference on world wide web. International World Wide Web Conferences Steering Committee.Google Scholar
  32. 32.
    Ge, L., Gao, J., Yu, X., Fan, W., & Zhang, A. (2012). Estimating local information trustworthiness via multi-source joint matrix factorization. In IEEE international conference on data mining. IEEE Computer Society.Google Scholar
  33. 33.
    Tanaka, K., Kawai, Y., Zhang, J., Nakajima, S., Inagaki, Y., Ohshima, H., et al. (2010). In Evaluating credibility of web information. Proceedings of the 4th international conference on uniquitous information management and communication—ICUIMC’10. Google Scholar
  34. 34.
    Ribeiro, R. A., Moreira, A. M., Broek, P. V. D., & Pimentel, A. (2011). Hybrid assessment method for software engineering decisions. Decision Support Systems,51(1), 208–219.CrossRefGoogle Scholar
  35. 35.
    Alvarez-Rodriguez, J. M., & Colomo-Palacios, R. (2014). Assessing professional skills in a multi-scale environment by means of graph-based algorithms. In 2014 European network intelligence conference.Google Scholar
  36. 36.
    Boshmaf, Y., Logothetis, D., Siganos, G., Lería, J., Lorenzo, J., Ripeanu, M., et al. (2016). Íntegro: Leveraging victim prediction for robust fake account detection in large scale OSNs. Computers & Security,61, 142–168.CrossRefGoogle Scholar
  37. 37.
    Cambridge, M. (2009). Sybilinfer: Detecting sybil nodes using social networks. NDSS The Internet Society.Google Scholar
  38. 38.
    Fire, M., Katz, G., & Elovici, Y. (2012). Strangers intrusion detection—detecting spammers and fake profiles in social networks based on topology anomalies. ASE Human Journal,11(11), 83–84.Google Scholar
  39. 39.
    Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine learning,29(2–3), 131–163.CrossRefGoogle Scholar
  40. 40.
    Cheng, J., & Greiner, R. (1999, July). Comparing Bayesian network classifiers. In Proceedings of the fifteenth conference on uncertainty in artificial intelligence (pp. 101–108). Morgan Kaufmann Publishers Inc.Google Scholar
  41. 41.
    Madden, M. G. (2009). On the classification performance of tan and general bayesian networks. Knowledge-Based Systems,22(7), 489–495.CrossRefGoogle Scholar
  42. 42.
    Nguyen, N. P., Yan, G., & Thai, M. T. (2013). Analysis of misinformation containment in online social networks. Computer Networks,57(10), 2133–2146.CrossRefGoogle Scholar
  43. 43.
    Brin, S. (1998). The anatomy of a large-scale hypertextual Web search engine. In International conference on world wide web. Elsevier Science Publishers B. V.Google Scholar
  44. 44.
    Kleinberg, M. (1997). Authoritative sources in a hyperlinked environment∗. J. ACM, 46.Google Scholar
  45. 45.
    Haveliwala, T. H. (2003). Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Transactions on Knowledge and Data Engineering,15(4), 784–796.CrossRefGoogle Scholar
  46. 46.
    Nikolaou, I. (2014). Social networking web sites in job search and employee recruitment. International Journal of Selection and Assessment,22(2), 179–189.CrossRefGoogle Scholar
  47. 47.
    Hraiz, R., Khader, M., & Shaout, A. (2019). A multi-stage fuzzy model for assessing applicants for faculty positions in universities. International Journal of Intelligent Information Technologies,15(1), 51–83.CrossRefGoogle Scholar
  48. 48.
    Hsieh, C.-J., Tiwari, M., Agarwal, D., Huang, X. L., & Shah, S. (2013). Organizational overlap on social networks and its applications. In Proceedings of the 22nd international conference on world wide web—WWW’13.Google Scholar
  49. 49.
    Freeman, D. M. (2017). Can you spot the fakes? In Proceedings of the 26th international conference on world wide web—WWW’17.Google Scholar
  50. 50.
    Adikari, S., & Dutta, K. (2019). Identifying fake profiles in LinkedIn. In Proceedings of PACIS 2014.Google Scholar

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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