mlsanityData to 7 Oct 2026

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

arXiv 2501.01284 ↗First posted 2 Jan 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 59 references that are in the map. First posted on arXiv 2 Jan 2025.

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26 of 59 references.

References · 26

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14 more
  1. 2019In Plain Sight: Media Bias Through the Lens of Factual Reportingcited by 68
  2. 2022POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detectioncited by 50
  3. 2024What is the Role of Small Models in the LLM Era: A Surveycited by 41
  4. 2022NeuS: Neutral Multi-News Summarization for Mitigating Framing Biascited by 26
  5. 2021MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristicscited by 24
  6. 2020Detecting Media Bias in News Articles using Gaussian Bias Distributionscited by 19
  7. 2019Tanbih: Get To Know What You Are Readingcited by 16
  8. 2020Context in Informational Bias Detectioncited by 15
  9. 2024IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicatorscited by 15
  10. 2024Beyond Partisan Leaning: A Comparative Analysis of Political Bias in Large Language Modelscited by 13
  11. 2021Political Depolarization of News Articles Using Attribute-aware Word Embeddingscited by 9
  12. 2024Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articlescited by 9
  13. 2021Mitigating Media Bias through Neutral Article Generationcited by 7
  14. 2023Mitigating Framing Bias with Polarity Minimization Losscited by 1

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