Unveiling Affective Polarization Trends in Parliamentary Proceedings

2025-12-04

AI Summary

This study addresses the persistent challenge of quantifying affective polarization by proposing a novel measurement paradigm grounded in discursive affective style rather than ideological positioning. Methodologically, it pioneers the systematic application of the Valence-Arousal-Dominance (VAD) three-dimensional affective model—drawn from affective computing—to parliamentary discourse analysis, leveraging NLP techniques to automate affective modeling of Hebrew-language debate transcripts from the Israeli Knesset. Its key contribution is the development of a replicable, cross-contextual operational framework for measuring affective polarization. Empirically, the analysis reveals statistically significant and temporally escalating divergence between governing and opposition parties across all three VAD dimensions: valence (increased negativity), arousal (heightened emotional intensity), and dominance (reduced perceived discourse control). These findings provide new empirical evidence and a methodological toolkit for investigating the affective mechanisms underlying democratic backsliding.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Recent years have seen an increase in polarized discourse worldwide, on various platforms. We propose a novel method for quantifying polarization, based on the emotional style of the discourse rather than on differences in ideological stands. Using measures of Valence, Arousal and Dominance, we detect signals of emotional discourse and use them to operationalize the concept of affective polarization. Applying this method to a recently released corpus of proceedings of the Knesset, the Israeli parliament (in Hebrew), we find that the emotional style of members of government differs from that of opposition members; and that the level of affective polarization, as reflected by this style, is significantly increasing with time.

Problem

• Quantifying affective polarization via emotional discourse style
• Detecting polarization signals using Valence, Arousal, Dominance measures
• Analyzing parliamentary proceedings to track rising affective polarization trends

Innovation

• Quantifies polarization via emotional discourse style
• Uses Valence, Arousal, Dominance measures for detection
• Applies method to parliamentary proceedings corpus