POLAR: a holistic framework for the modelling of polarization and identification of polarizing topics in news media

Abstract

Polarization is an alarming trend in modern societies with serious implications on social cohesion and the democratic process. Typically, polarization manifests itself in the public discourse in politics, governance and ideology. In recent years, however, polarization arises increasingly in a wider range of issues, from identity and culture to healthcare and the environment. As the public and private discourse moves online, polarization feeds in and is fed by phenomena like fake news and hate speech. The identification and analysis of online polarization is challenging because of the massive scale, diversity, and unstructured nature of online content, and the rapid and unpredictable evolution of polarizing issues. Therefore, we need effective ways to identify, quantify, and represent polarization and polarizing topics algorithmically and at scale. In this work, we introduce POLAR - an unsupervised, large-scale framework for modeling and identifying polarizing topics in any domain, without prior domain-specific knowledge. POLAR comprises a processing pipeline that analyzes a corpus of an arbitrary number of news articles to construct a hierarchical knowledge graph that models polarization and identify polarizing topics discussed in the corpus. Our evaluation shows that POLAR is able to identify and rank polarizing topics accurately and efficiently.