{
  "annotation_id": "487d7fc5-6399-43a7-8c16-8edcec536f3a",
  "date_created": "2026-02-17T05:53:04.775000Z",
  "date_modified": "2026-02-17T05:53:04.775000Z",
  "file_hash": "6a56de0c28be1cc4a593f1f63d0bca6012148c7bea6eedf94c285fc7bdcfa151",
  "private": false,
  "record": {
    "abstract": "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.",
    "arxiv_id": "2601.05358",
    "authors": [
      "Tim Menzner",
      "Jochen L. Leidner"
    ],
    "categories": [
      "cs.CL",
      "cs.CY"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "The Table of Media Bias Elements: A sentence-level taxonomy of media bias types and propaganda techniques",
    "url": "https://arxiv.org/abs/2601.05358",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "308440a2-2cc1-46c3-a33d-3b2f26e234ad",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}