Into the Battlefield: Quantifying and Modeling Intra-community Conflicts in Online Discussion

TL;DR

This paper introduces a continuous measure of conflict in online discussions, predicting conflict levels for news articles and user interactions using machine learning, and analyzing conflict dynamics in Reddit communities.

Contribution

It presents novel continuous conflict quantification and models for predicting conflict in online discussions, validated on a large Reddit dataset.

Findings

Support vector machine achieves 0.89 AUC in conflict prediction.

Graph convolutional model achieves 0.69 AUC for user conflict prediction.

Insights into conflict formation, temporal dynamics, and language contributions.

Abstract

In this work, we present a novel quantification of conflict in online discussion. Unlike previous studies on conflict dynamics, which model conflict as a binary phenomenon, our measure is continuous-valued, which we validate with manually annotated ratings. We address a two-way prediction task. Firstly, we predict the probable degree of conflict a news article will face from its audience. We employ multiple machine learning frameworks for this task using various features extracted from news articles. Secondly, given a pair of users and their interaction history, we predict if their future engagement will result in a conflict. We fuse textual and network-based features together using a support vector machine which achieves an AUC of 0.89. Moreover, we implement a graph convolutional model which exploits engagement histories of users to predict whether a pair of users who never met each other before will have a conflicting interaction, with an AUC of 0.69. We perform our studies on a massive discussion dataset crawled from the Reddit news community, containing over 41k news articles and 5.5 million comments. Apart from the prediction tasks, our studies offer interesting insights on the conflict dynamics -- how users form clusters based on conflicting engagements, how different is the temporal nature of conflict over different online news forums, how is contribution of different language based features to induce conflict, etc. In short, our study paves the way towards new methods of exploration and modeling of conflict dynamics inside online discussion communities.