Measuring quality, reputation and trust in online communities

TL;DR

This paper introduces a novel iterative ranking method that evaluates user reputation and content quality in online communities by leveraging trust relationships, demonstrated on real-world forums and music platforms.

Contribution

The paper presents a new ranking algorithm that integrates trust data to improve assessment of user reputation and content quality in online social networks.

Findings

Trust relationships enhance ranking accuracy

Algorithm variants influence ranking outcomes

Method effective on real online communities

Abstract

In the Internet era the information overload and the challenge to detect quality content has raised the issue of how to rank both resources and users in online communities. In this paper we develop a general ranking method that can simultaneously evaluate users' reputation and objects' quality in an iterative procedure, and that exploits the trust relationships and social acquaintances of users as an additional source of information. We test our method on two real online communities, the EconoPhysics forum and the Last.fm music catalogue, and determine how different variants of the algorithm influence the resultant ranking. We show the benefits of considering trust relationships, and define the form of the algorithm better apt to common situations.