Tracing Partisan Bias to Its Emotional Fingerprints: A Computational Approach to Mitigation

TL;DR

This paper presents a computational framework that identifies and mitigates media bias by analyzing emotional language in news texts, using the Valence-Arousal-Dominance model to neutralize partisan emotional fingerprints.

Contribution

It introduces a novel method to detect and neutralize emotional biases in media by quantifying emotional fingerprints and applying a targeted summarization model.

Findings

NeutraSum effectively removes partisan emotional fingerprints from summaries.

Distinct emotional fingerprints are statistically significant for different media orientations.

The framework achieves lower emotional bias scores compared to other models.

Abstract

This study introduces a novel framework for analysing and mitigating media bias by tracing partisan stances to their linguistic roots in emotional language. We posit that partisan bias is not merely an abstract stance but materialises as quantifiable 'emotional fingerprints' within news texts. These fingerprints are systematically measured using the Valence-Arousal-Dominance (VAD) framework, allowing us to decode the affective strategies behind partisan framing. Our analysis of the Allsides dataset confirms this hypothesis, revealing distinct and statistically significant emotional fingerprints for left, centre, and right-leaning media. Based on this evidence-driven approach, we then propose a computational approach to mitigation through NeutraSum, a model designed to neutralise these identified emotional patterns. By explicitly targeting the VAD characteristics of biased language, NeutraSum generates summaries that are not only coherent but also demonstrably closer to an emotionally neutral baseline. Experimental results validate our framework: NeutraSum successfully erases the partisan emotional fingerprints from its summaries, achieving a demonstrably lower emotional bias score than other models. This work pioneers a new path for bias mitigation, shifting the focus from treating symptoms (political labels) to addressing the cause: the emotional encoding of partisan bias in language.