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

机译：传播信任和不信任以检测社交网络中的巨魔

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摘要

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.
机译：由于过去几年中这些在线社区的巨大扩展，信任和信誉系统已成为许多社交网络的重要组成部分。由于这种增长，一些用户试图扰乱这些社区的正常气氛，甚至利用它们来获得某种好处。因此，信任的概念是在线系统（例如，在线市场，评论聚合器，社交新闻站点和论坛）的性能中的关键点。在这项工作中，我们提出了一种方法来计算社交网络中用户的可信度排名。我们方法的目的是通过降低恶意软件用户的排名来防止其在网络中获得较高的声誉。我们提出了一种新颖的系统，旨在通过网络传播用户的正面和负面意见，以使每个用户对其他人的观点都会影响他们的整体信任度。我们的建议已在不同的挑战性情况下进行了评估。实验包括随机图的生成，从社交新闻站点提取的真实世界数据集的使用以及真实数据集和生成技术的组合，以便在不同的环境中测试我们的建议。结果表明，我们的方法在每种情况下均表现良好，表明信任和不信任的传播是信任和信誉系统中的可靠机制。

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  • 来源
    《Computer networks》 |2012年第12期|p.2884-2895|共12页
  • 作者

    F. Javier Ortega; Jose A. Troyano; Fermin L Cruz; Carlos G. Vallejo; Fernando Enriquez;

  • 作者单位

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

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

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

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

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

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    social networks; trust and reputation systems; graph theory; ranking algorithms;

    机译：社交网络;信任和声誉系统;图论排名算法;

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