构建细粒度媒体偏见语句分类体系，揭示新闻语言中的隐性偏见。
The Table of Media Bias Elements: A sentence-level taxonomy of media bias types and propaganda techniques
- 基于2.6万句新闻语料，通过多轮人工标注与理论整合，提炼出38种具体偏见类型。
- 发现不同偏见类型在样本中分布不均，部分类型如情绪化措辞更常见。
- 提供可操作的识别指南，适合研究者、媒体从业者和批判性读者使用。
公众讨论‘左翼’或‘右翼’新闻时，往往忽视偏见通常通过具体的语言手段表达，而这些手段超越单一政治光谱。因此，我们从媒体立场转向句子层面的语言偏见表达方式。基于从新闻室语料库、用户提交及自主浏览收集的26,464个句子，结合细致阅读、跨学科理论与初步标注，迭代构建了一个细粒度的句子级媒体偏见与宣传技巧分类体系。最终成果为一个两级架构，包含38种基本偏见类型，分为六大功能类别，并可视化为“媒体偏见要素表”。每种类型均配有定义、真实案例、认知与社会动因说明及识别指引。对随机抽取的155句样本进行量化调查，揭示各类偏见的出现频率差异；与现有主流NLP与传播学分类体系交叉比对，显示覆盖范围显著提升且歧义减少。
原文摘要 · Abstract (English)
Public debates about "left-" or "right-wing" news overlook the fact that bias is usually conveyed by concrete linguistic manoeuvres that transcend any single political spectrum. We therefore shift the focus from where an outlet allegedly stands to how partiality is expressed in individual sentences. Drawing on 26,464 sentences collected from newsroom corpora, user submissions and our own browsing, we iteratively combine close-reading, interdisciplinary theory and pilot annotation to derive a fine-grained, sentence-level taxonomy of media bias and propaganda. The result is a two-tier schema comprising 38 elementary bias types, arranged in six functional families and visualised as a "table of media-bias elements". For each type we supply a definition, real-world examples, cognitive and societal drivers, and guidance for recognition. A quantitative survey of a random 155-sentence sample illustrates prevalence differences, while a cross-walk to the best-known NLP and communication-science taxonomies reveals substantial coverage gains and reduced ambiguity.
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