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What's in the News? Towards Identification of Bias by Commission, Omission, and Source Selection (COSS)

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arxiv 2508.02540 v1 pith:KMHCJNM5 submitted 2025-08-04 cs.CL

classification cs.CL
keywords biasnewscommissioncossidentificationinformationomissionselection
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis

    cs.AI 2026-06 conditional novelty 6.0 of 10

    LLM compression of filings and earnings calls often changes the source-implied bear/neutral/bull decision; agentic multi-candidate auditing against the source reduces those flips.

  2. When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis

    cs.AI 2026-06 unverdicted novelty 5.0 of 10

    LLM-based compression of financial source material can alter downstream investment decisions via decontextualization and model dependency, addressed by an agentic auditing approach that checks multiple compressions ag...

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