NLP-Driven Approaches to Measuring Online Polarization and Radicalization

Abstract

The growing popularity of social media has coincided with a massive number of real-world issues and crises that are controversial and polarizing. Recent issues such as Russo-Ukrainian and Israeli-Palestinian conflicts, alongside classic issues such as abortion-ban and gun-control, have raised heated debates offline and online. Throughout the past two decades, Computational Social Scientists have been introducing methods of modeling and measuring online polarization and radicalization. Yet, most of the proposed methods rely on traditional tools such as graph analysis and classic NLP models. These tools are accompanied by limitations in terms of scalability, granularity, and availability of data (e.g., follow network is no longer publicly available on Twitter).

Fortunately, in the past few years, thanks to the invention of the transformers architecture, the world has witnessed massive breakthroughs in the field of Natural Language Processing (NLP). Especially, Large Language Models (LLMs) have grasped the attention of both public and scientific communities. These breakthroughs have also created unprecedented opportunities for advancing classic techniques in various domains of Computational Social Sciences, including polarization detection and opinion mining.

This thesis aims to propose novel approaches using state-of-the-art NLP techniques to model and track polarization on social media. It introduces a scalable method for quantifying echo chambers with sentence transformers, revealing asymmetries in discourse diversity across political ideologies. Furthermore, it applies LLMs to analyze the content of cross-partisan interactions, showing that cross-party engagement does not necessarily lead to productive discourse. The thesis also investigates radicalization in gender-based communities and compares the spread of radical content across platforms like Reddit and Discord. Lastly, it addresses the limitations of existing language models in detecting stance polarity by fine-tuning a sentence transformer to become stance-aware, enabling more accurate detection of opposing viewpoints on similar topics. Together, these contributions offer Computational Social Scientists new tools for understanding polarization, radicalization, and bias in online environments.