  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.
