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.

翻译：关于“左翼”或“右翼”新闻的公共讨论往往忽略了一个事实：偏见通常通过超越单一政治光谱的具体语言策略来传递。因此，我们将关注点从媒体机构的所谓立场转向偏颇性在单个句子中的表达方式。基于从新闻语料库、用户提交内容及自主浏览收集的26,464个句子，我们通过迭代式细读、跨学科理论与试点标注相结合的方法，推导出一个细粒度的句子级媒体偏见与宣传分类体系。成果呈现为包含38种基础偏见类型的双层架构，这些类型按六大功能族进行组织，并以“媒体偏见元素表”形式实现可视化。针对每种类型，我们提供了定义、现实案例、认知与社会驱动因素以及识别指南。对随机抽取的155个句子样本进行的定量调查展示了不同偏见类型的流行度差异，而与最著名的自然语言处理及传播科学分类体系的对照分析，则揭示了本体系在覆盖范围上的显著提升与歧义性的有效降低。