Trolls Identification within an Uncertain Framework

TL;DR

This paper introduces a novel method for detecting online trolls within social communities by applying belief function theory to analyze message conflicts, aiming to help moderators maintain a healthy online environment.

Contribution

It proposes a new uncertain framework-based approach for identifying malicious users, specifically trolls, using conflict measures in belief function theory applied to discussion threads.

Findings

Effective detection of trolls in simulated data

Demonstrates feasibility of belief function conflict measures

Enhances community moderation strategies

Abstract

The web plays an important role in people's social lives since the emergence of Web 2.0. It facilitates the interaction between users, gives them the possibility to freely interact, share and collaborate through social networks, online communities forums, blogs, wikis and other online collaborative media. However, an other side of the web is negatively taken such as posting inflammatory messages. Thus, when dealing with the online communities forums, the managers seek to always enhance the performance of such platforms. In fact, to keep the serenity and prohibit the disturbance of the normal atmosphere, managers always try to novice users against these malicious persons by posting such message (DO NOT FEED TROLLS). But, this kind of warning is not enough to reduce this phenomenon. In this context we propose a new approach for detecting malicious people also called 'Trolls' in order to allow community managers to take their ability to post online. To be more realistic, our proposal is defined within an uncertain framework. Based on the assumption consisting on the trolls' integration in the successful discussion threads, we try to detect the presence of such malicious users. Indeed, this method is based on a conflict measure of the belief function theory applied between the different messages of the thread. In order to show the feasibility and the result of our approach, we test it in different simulated data.