Information Systems Frontiers

, Volume 17, Issue 6, pp 1381–1400 | Cite as

Estimating trust value: A social network perspective

  • Wei-Lun Chang
  • Arleen N. Diaz
  • Patrick C. K. Hung


This research introduces the concept of social distance, which is drawn from clustering methods applied to the social network user base; and incorporates distance in the estimation of trust, as well as user-generated ratings. The trust value estimated will serve as a metric for filtering and sorting content of any kind based on the trustworthiness of the creator. The results revealed that it is possible to provide an estimated measure of trust within individuals in a social network, that clustering methods were of significant help into said evaluation, and that the integration of other variables affecting the building of trust. Results also showed that higher rating scores combined with shorter social distances provide satisfactory trust values, while the opposite happened for subjects presenting lower rating scores in combination with longer distances. This study contributes to the current literature on trust estimation and social networks role in such endeavors.


Trust value Social network Self-organizing maps Online rating systems 


  1. Abdul-Rahman, A., & Hailes, S. (1997). A Distributed Trust Model. (T. Haigh, B. Blakley, M. E. Zurbo, & C. Meodaws, Eds.) Proceedings of the 1997 workshop on New security paradigms NSPW 97, 48–60.Google Scholar
  2. Bobadilla, J., Serradilla, F., & Bernal, J. (2010). A new collaborative filtering metric that improves the behavior of recommender systems. Knowledge-Based Systems, 23(6), 520–528.CrossRefGoogle Scholar
  3. Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. (G. Cooper & S. Moral, Eds.) Proceedings of the 14th conference on Uncertainty in Artificial Intelligence, 461(8), 43–52, San Francisco, CA.Google Scholar
  4. Caverlee, J., Liu, L., & Webb, S. (2010). The Social Trust framework for trusted social information management: Architecture and algorithms. Information Sciences, 180(1), 95–112.CrossRefGoogle Scholar
  5. Chen, S., & Dillon, C. (2003). Interpreting Dimensions of Consumer Trust in E-Commerce. Information Technology and Management, 4(2–3), 303–318.CrossRefGoogle Scholar
  6. comScoree (2007). “Online Consumer-Generated Reviews Have Significant Impact on Offline Purchase Behavior,” press release, (November 29). =1928
  7. DuBois, T., Golbeck, J., Kleint, J., & Srinivasan, A. (2009). Improving Recommendation Accuracy by Clustering Social Networks with Trust. In Proceedings of the ACM RecSys 2009 Workshop on Recommender Systems and the Social Web, October.Google Scholar
  8. Golbeck, J. (2006). Generating Predictive Movie Recommendations from Trust in Social Networks. iTrust'06 Proceedings of the 4th international conference on Trust Management, 3986, pp. 93–104.Google Scholar
  9. Golbeck, J. (2009). Trust and nuanced profile similarity in online social networks. ACM Transactions on the Web, 3(4), 12–33.CrossRefGoogle Scholar
  10. Gross, R., & Acquisti, A. (2005). Information revelation and privacy in online social networks. Human Factors, 0707(November), 71.Google Scholar
  11. Herbig, P. A., & Kramer, H. (1994). The Effect of Information Overload on the Innovation Choice Process: Innovation Overload. Journal of Consumer Marketing, 11(2), 45–54.CrossRefGoogle Scholar
  12. Jiang, W., Wang, G., & Wu, J. (2014). Generating trusted graphs for trust evaluation in online social networks. Future Generation Computer Systems, 31, 48–58.CrossRefGoogle Scholar
  13. Jin, S., Park, C., Choi, D., Chung, K., & Yoon, H. (2005). Cluster-based trust evaluation scheme in an ad hoc network. ETRI Journal, 27(4), 465–468.CrossRefGoogle Scholar
  14. Kate S. (2009), “Trustworthiness within social networking sites: A study on the intersection of hci and sociology,” Master ThesisGoogle Scholar
  15. Katz, E., & Lazarsfeld, P. F. (1955). Personal influence: The part played by people in the flow of mass communications. New York: Free Press.Google Scholar
  16. Kautz, H., Selman, B., & Shah, M. (1997). Combining Social Networks and Collaborative Filtering. Communications of the ACM, 40(3), 63–65.CrossRefGoogle Scholar
  17. Kim, Y. A., & Ahmad, M. A. (2013). Trust, distrust and lack of confidence of users in online social media-sharing communities. Knowledge-Based Systems, 37, 438–450.CrossRefGoogle Scholar
  18. Kim, Y. A., & Phalak, R. (2012). A trust prediction framework in rating-based experience sharing social networks without a Web of Trust. Information Sciences, 191, 128–145.CrossRefGoogle Scholar
  19. Kohonen, T. (1990). The Self-Organizing Map. Proceedings of the IEEE, 78(9), 1464–1480.CrossRefGoogle Scholar
  20. Lagus, K., Honkela, T., Kaski, S., & Kohonen, T. (1996). Self-organizing maps of document collections: A new approach to interactive exploration. Neural Networks, 1(2), 238–243.Google Scholar
  21. Lathia, N., Hailes, S., & Capra, L. (2008). Trust-based collaborative filtering. Trust Management II, 263, 299–300.Google Scholar
  22. Liu, F., & Lee, H. J. (2010). Use of social network information to enhance collaborative filtering performance. Expert Systems with Applications, 37(7), 4772–4778.CrossRefGoogle Scholar
  23. Liu, H., & Maes, P. (2005). Paper presented at the IUI’05. San Diego: California. Network. InterestMap: Harvesting Social Network Profiles for Recommendations.Google Scholar
  24. Lu, Y., Tsaparas, P., Ntoulas, A., & Polanyi, L. (2010). Exploiting social context for review quality prediction. Proceedings of the 19th international conference on World wide web WWW 10, 15(4), 691.Google Scholar
  25. Ma, H., Zhou, D., Liu, C., Lyu, M. R., & King, I. (2011). Recommender systems with social regularization. (I. King, W. Nejdl, & H. Li, Eds.) WSDM '11 Proceedings of the fourth ACM international conference on Web search and data mining, February 9–12, 2011, Hong Kong, China, pp. 287–296Google Scholar
  26. Massa, P., & Avesani, P. (2004). Trust-aware collaborative filtering for recommender systems. (R. Meersman & Z. Tari, Eds.) On the Move to Meaningful Internet Systems 2004 CoopIS DOA and ODBASE, 3290, pp. 492–508.Google Scholar
  27. Massa, P., & Bhattacharjee, B. (2004). Using trust in recommender systems: an experimental analysis. (C. D. Jensen, S. Poslad, & T. Dimitrakos, Eds.)Trust Management, 2995, pp. 221–235.Google Scholar
  28. McKnight, H., & Chervany N. (2001). Trust and distrust definitions: One bite at a time. In: Falcone, R., Singh M.P., & Tan Y.H. (Eds.) Trust in Cyber-Societies: Integrating the Human and Artificial Perspectives, pp. 27–54.Google Scholar
  29. Melville, P., & Sindhwani, V. (2010). Recommender Systems. Claude Sammut and Geoffrey Webb (Eds), Springer: Encyclopedia of Machine Learning.Google Scholar
  30. Meo, P. D., Graziella, V., Feo, L., Calabria, R., Nocera, A., Quattrone, G., Rosaci, D., et al. (2009). Finding reliable users and social networks in a social internetworking system. Social Networks, 173–181.Google Scholar
  31. Montaner, M., López, B., & De La Rosa, J. L. (2002). Developing trust in recommender agents. (M. Gini, T. Ishida, C. Castelfranchi, & W. L. Johnson, Eds.) Proceedings of the first international joint conference on Autonomous agents and multiagent systems part 1 AAMAS 02, 304.Google Scholar
  32. Mui, L., Mohtashemi, M., Ang, C., Szolovits, P., & Halberstadt, A. (2001). Ratings in Distributed Systems: A Bayesian Approach. Encounter, 1–7.Google Scholar
  33. Nambisan, S., & Nambisan, P. (2008). How to profit from a better virtual customer environment”. MIT Sloan Management Review, 49(3), 53–61.Google Scholar
  34. Nielsen Media. (2011) Social Media Report: Q3 2011. Nielsen Wire.
  35. O’Donovan, J., & Smyth, B. (2005). Trust in recommender systems. Proceedings of the 10th international conference on Intelligent user interfaces IUI 05, 15, 167.Google Scholar
  36. Ortega, F. J., Troyano, J. A., Cruz, F. L., Vallejo, C. G., & Enríquez, F. (2012). Propagation of trust and distrust for the detection of trolls in a social network. Computer Networks, 56, 2884–2895.CrossRefGoogle Scholar
  37. Pham, M. C., Cao, Y., Klamma, R., & Jarke, M. (2010). A Clustering Approach for Collaborative Filtering Recommendation Using Social Network Analysis. Journal Of Universal Computer Science, 17(4), 1–21.Google Scholar
  38. Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: A cross-discipline view of trust. Academy of Management Review, 23(3), 393–404.CrossRefGoogle Scholar
  39. Rumelhart, D. E., & McClelland, J. L. (1986). Parallel Distributed Processing. (M. I. T. Press, Ed.) Foundations (Vol. 1, pp. 135–153). MIT Press.Google Scholar
  40. Ryu, Y., Kim, H. K., Cho, Y. H., & Kim, J. K. (2006). Peer-oriented content recommendation in a social network. In Paper presented at the 16th workshop on information technologies and systems (WITS 2006), pp. 115–120, 2006.Google Scholar
  41. Sarwar, B. M., Karypis, G., Konstan, J., & Riedl, J. (2002). Recommender Systems for Large-scale E-Commerce : Scalable Neighborhood Formation Using Clustering, In Paper presented at The Fifth International Conference on Computer and Information Technology (ICCIT 2002), pp. 393–402.Google Scholar
  42. Sashi, C. M. (2012). "Customer engagement, buyer-seller relationships, and social media", Management Decision, Vol. 50 Iss: 2, pp.253 - 272.Google Scholar
  43. Scherchan, W., Nepal, S., & Paris, C. (2013). A Survey of Trust in Social Networks. ACM Computing Surveys, 45(4), 1–33.CrossRefGoogle Scholar
  44. Schultz, T. (2011). Preface. Annual Review of Entomology. Annual Reviews, A Nonprofit Scientific Publisher. Vol. 56.Google Scholar
  45. Sinha, R., & Swearingen, K. (2001). Comparing Recommendations Made by Online Systems and Friends. In Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries. Ireland: Dublin.Google Scholar
  46. Stanier, J., Naicken, S., Basu, A., Li, J., & Wakeman, I. (2010). Can We Use Trust in Online Dating? Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications, 1(4), 50–61.Google Scholar
  47. Tan, F. & Sutherland, P. (2004). Online Consumer Trust: A Multi-Dimensional Model. IGI Global; Auckland University of Technology, Advanced Topics in Electronic Commerce, Chapter 10, pp. 188–208.Google Scholar
  48. Thackeray, R., Neiger, B. I., Hanson, C. L., & McKenzie, J. F. (2008). Enhancing promotional strategies within social marketing programs: use of Web 2.0 social media”. Health Promotion Practice, 9(4), 338–343.CrossRefGoogle Scholar
  49. Yu, J. B..& Xi L. F. (2008). Using an MQE chart based on a self-organizing map to monitor out-of-control signals in manufacturing processes. International Journal of Production Research. Vol. 46, Iss. 21, pp. 5907–5933.Google Scholar
  50. Zhai, D., & Pan, H. (2008). A Social Network-Based Trust Model for E-Commerce. In 2008 4th International Conference on Wireless Communications Networking and Mobile Computing, (70639002) (pp. 1–5).Google Scholar
  51. Zhao, X., Li, P., & Kohonen, T. (2011). Contextual self-organizing map: software for constructing semantic representations. Behavior Research Methods, 43(1), 77–88.CrossRefGoogle Scholar
  52. Zheng, X., Zeng, D., & Wang, F. Y. (2014). Social Balance in Signed Networks. Information Systems Frontiers: Online version.Google Scholar
  53. Ziegler, C. N., & Lausen, G. (2005). Propagation Models for Trust and Distrust in Social Networks. Information Systems Frontiers, 7(4–5), 337–358.CrossRefGoogle Scholar
  54. Ziegler, C. N., & Golbeck, J. (2007). Investigating Correlations of Trust and Interest Similarity. Decision Support Systems, 43, 460–475.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Wei-Lun Chang
    • 1
  • Arleen N. Diaz
    • 1
  • Patrick C. K. Hung
    • 2
  1. 1.Department of Business AdministrationTamkang UniversityNew Taipei CityTaiwan
  2. 2.Faculty of Business and Information TechnologyUniversity of Ontario Institute of Technology (UOIT)OshawaCanada

Personalised recommendations