Predicting Trust and Distrust in Social Networks

Abstract

Abstract-As user-generated content and interactions have overtaken the web as the default mode of use, questions of whom and what to trust have become increasingly important. Fortunately, online social networks and social media have made it easy for users to indicate whom they trust and whom they do not. However, this does not solve the problem since each user is only likely to know a tiny fraction of other users; we must have methods for inferring trust -and distrust -between users who do not know one another. In this paper, we present a new method for computing both trust and distrust (i.e., positive and negative trust). We do this by combining an inference algorithm that relies on a probabilistic interpretation of trust based on random graphs with a modified spring-embedding algorithm. Our algorithm correctly classifies hidden trust edges as positive or negative with high accuracy. These results are useful in a wide range of social web applications where trust is important to user behavior and satisfaction.

Social Media and Trust — A Systematic Literature Review

“…A possible explanation is that distrust is difficult to detect because it is a transient property in network relationships (Bian et al, 2009). There are also no accurate methods for measuring inferred distrust between social media users (DuBois, Golbeck & Srinivasan, 2011). Another potential explanation is that people use social media as a way to control information and relationships and thereby mitigate uncertainty (Tidwell & Walther, 2002) early on screening out any relationship that would have potential to breed distrust.…”

Inspectability and control in social recommenders

“…Arguably, this method allows users to indicate how much they "trust" their friends' preferences in the recommendation domain. Several researchers have investigated this idea of assigning trust scores to friends in collaborative recommenders, through explicit mechanisms such as in Golbeck's FilmTrust system [14] which can support propagation of trust scores around a network of peers, and through automated mechanisms for modeling trust such as [10,36]. Several recent studies have extended these ideas to prediction of personality, and by derivation, behavior of a user within the system [1] in terms of both trust and distrust [15].…”

Modelling Trust in Semantic Web Applications

“…An alternative proposed by [17] applies a probablistic method to postulate the existence of a path between origin and sink with a seperate spring-embedding layout alogrithm. The latter provides a spatial dimension with directly connected nodes being attracted or repelled from each other based on their trust values.…”