用情绪语言分析新闻偏见，让摘要更中立。
Tracing Partisan Bias to Its Emotional Fingerprints: A Computational Approach to Mitigation
- 通过情绪三维度框架量化新闻中的情感指纹。
- 发现左右翼媒体有显著不同的情绪特征。
- 提出NeutraSum模型主动消除情感偏见，适合内容审核者。
本研究提出一种新框架，通过追踪新闻文本中情感语言的‘情绪指纹’来分析和缓解媒体偏见。我们认为，政治偏见不仅是一种立场，更以可量化的‘情绪指纹’形式体现在语言中。利用情感三维度（效价-唤醒-支配力，VAD）框架，系统测量这些指纹，揭示了左、中、右倾媒体在情绪策略上的差异。基于此，我们提出NeutraSum模型，通过显式抑制偏差语言的VAD特征，生成更接近情绪中性基线的摘要。实验验证：该模型能有效消除原文中的政党情绪指纹，其情感偏见得分显著低于其他模型。这项工作开创性地将偏见治理从处理表象（政治标签）转向根治根源——语言中的情绪编码。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。