Argumentation Reasoning with Graph Neural Networks for Reddit Conversation Analysis

Abstract

The automated analysis of different trends in online debating forums is an interesting tool for sampling the agreement between citizens in different topics. In these online debating forums, users post different comments and answers to previous comments of other users. In previous work, we have defined computational models to measure different values in these online debating forums. A main ingredient in these models has been the identification of the set of winning posts trough an argumentation problem that characterizes this winning set trough a particular argumentation acceptance semantics. In the argumentation problem we first associate the online debate to analyze as a debate tree. Then, comments are divided in two groups, the ones that agree with the root comment of the debate, and the ones that disagree with it, and we extract a bipartite graph where the unique edges are the disagree edges between comments of the two different groups. Once we compute the set of winning posts, we compute the different measures we are interested to get from the debate, as functions defined over the bipartite graph and the set of winning posts. In this work, we propose to explore the use of graph neural networks to solve the problem of computing these measures, using as input the debate tree, instead of our previous argumentation reasoning system that works with the bipartite graph. We focus on the particular online debate forum Reddit, and on the computation of a measure of the polarization in the debate. Our results over a set of Reddit debates, show that graph neural networks can be used with them to compute the polarization measure with an acceptable error, even if the number of layers of the network is bounded by a constant.