A Community-Based Approach for Stance Distribution and Argument Organization

TL;DR

This paper introduces an unsupervised graph-based system that organizes arguments from online debates into communities, helping users understand diverse viewpoints without requiring training data.

Contribution

It presents a novel community detection method for argument organization that captures multiple relationship types and simplifies complex debates for better comprehension.

Findings

Successfully identifies meaningful argument communities.

Effectively processes hundreds of articles without training data.

Facilitates understanding of complex socio-political debates.

Abstract

The proliferation of online debate platforms and social media has led to an unprecedented volume of argumentative content on controversial topics from multiple perspectives. While this wealth of perspectives offers opportunities for developing critical thinking and breaking filter bubbles (Pariser 2011), the sheer volume and complexity of arguments make it challenging for readers to synthesize and comprehend diverse viewpoints effectively. We present an unsupervised graph-based approach for community-based argument organization that helps users navigate and understand complex argumentative landscapes. Our system analyzes collections of topic-focused articles and constructs a rich interaction graph by capturing multiple relationship types between arguments: topic similarity, semantic coherence, shared keywords, and common entities. We then employ community detection to identify argument communities that reveal homogeneous and heterogeneous viewpoint distributions. The detected communities are simplified through strategic graph operations to present users with digestible, yet comprehensive summaries of key argumentative patterns. Our approach requires no training data and can effectively process hundreds of articles while preserving nuanced relationships between arguments. Experimental results demonstrate our system's ability to identify meaningful argument communities and present them in an interpretable manner, facilitating users' understanding of complex socio-political debates.