{"ID":2887012,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.02540","arxiv_id":"2508.02540","title":"What's in the News? Towards Identification of Bias by Commission, Omission, and Source Selection (COSS)","abstract":"In a world overwhelmed with news, determining which information comes from reliable sources or how neutral is the reported information in the news articles poses a challenge to news readers. In this paper, we propose a methodology for automatically identifying bias by commission, omission, and source selection (COSS) as a joint three-fold objective, as opposed to the previous work separately addressing these types of bias. In a pipeline concept, we describe the goals and tasks of its steps toward bias identification and provide an example of a visualization that leverages the extracted features and patterns of text reuse.","short_abstract":"In a world overwhelmed with news, determining which information comes from reliable sources or how neutral is the reported information in the news articles poses a challenge to news readers. In this paper, we propose a methodology for automatically identifying bias by commission, omission, and source selection (COSS) a...","url_abs":"https://arxiv.org/abs/2508.02540","url_pdf":"https://arxiv.org/pdf/2508.02540v1","authors":"[\"Anastasia Zhukova\",\"Terry Ruas\",\"Felix Hamborg\",\"Karsten Donnay\",\"Bela Gipp\"]","published":"2025-08-04T15:47:17Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[]","has_code":false}