Propagation of trust and distrust for the detection of trolls in a social network

  • Authors:

  • F. Javier Ortega;José A. Troyano;FermíN L. Cruz;Carlos G. Vallejo;Fernando EnríQuez

  • Affiliations:

  • Department of Computer Languages and Systems, University of Seville, Avda. Reina Mercedes s/n, 41012 Seville, Spain;Department of Computer Languages and Systems, University of Seville, Avda. Reina Mercedes s/n, 41012 Seville, Spain;Department of Computer Languages and Systems, University of Seville, Avda. Reina Mercedes s/n, 41012 Seville, Spain;Department of Computer Languages and Systems, University of Seville, Avda. Reina Mercedes s/n, 41012 Seville, Spain;Department of Computer Languages and Systems, University of Seville, Avda. Reina Mercedes s/n, 41012 Seville, Spain

  • Venue:
  • Computer Networks: The International Journal of Computer and Telecommunications Networking
  • Year:
  • 2012

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Abstract

Trust and Reputation Systems constitute an essential part of many social networks due to the great expansion of these on-line communities in the past few years. As a consequence of this growth, some users try to disturb the normal atmosphere of these communities, or even to take advantage of them in order to obtain some kind of benefits. Therefore, the concept of trust is a key point in the performance of on-line systems such as on-line marketplaces, review aggregators, social news sites, and forums. In this work we propose a method to compute a ranking of the users in a social network, regarding their trustworthiness. The aim of our method is to prevent malicious users from illicitly gaining high reputation in the network by demoting them in the ranking of users. We propose a novel system intended to propagate both positive and negative opinions of the users through a network, in such way that the opinions from each user about others influence their global trust score. Our proposal has been evaluated in different challenging situations. The experiments include the generation of random graphs, the use of a real-world dataset extracted from a social news site, and a combination of both a real dataset and generation techniques, in order to test our proposals in different environments. The results show that our method performs well in every situations, showing the propagation of trust and distrust to be a reliable mechanism in a Trust and Reputation System.