CrowdsouRS: A crowdsourced reputation system for identifying deceptive online contents

Abstract

In recent years, accelerated web-based technologies have revolutionized content generation and broadcast mecha-nisms through the Internet. Social media, blogs, e-newspaper, auction sites facilitate the creation and exchange of user-generated contents, which rarely go through any fact-finding mechanism or rigorous editorial process. This has fuelled the creation and publication of fake news in the web. The proliferation of social networks has been exploited to accelerate the distribution and propagation of such fake news at an unprecedented level, creating a major concern for the web. There have been several efforts undertaken to rectify this problem, unfortunately, none seems to be effective to root out this concerning issue. In this paper, we present CrowdsouRS, a Crowd-sourced Reputation System, implemented as a browser extension, for the web that leverages the wisdom of the crowd to identify and tag deceptive online contents. It aggregates reputation scores for a web page from multiple users, which is then visualized in order to help other users to determine if the contents of the web page are deceptive. We have evaluated the usability and effectiveness of CrowdsouRS with a number of users and our evaluations suggest that users find the tool useful in serving its purpose.