Tracing Partisan Bias to Its Emotional Fingerprints: A Computational Approach to Mitigation
Junjie Liu, Xi Luo, Sirong Wu, Gengchen Sun, and Yuhui Deng

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,…
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Taxonomy
TopicsComputational and Text Analysis Methods · Sentiment Analysis and Opinion Mining · Misinformation and Its Impacts
