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

arXiv: https://arxiv.org/abs/2508.02540 · HTML: https://mlsanity.com/paper/2508.02540/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [The POLUSA Dataset: 0.9M Political News Articles Balanced by Time and Outlet Popularity](https://mlsanity.com/paper/2005.14024/) (2020)
- [NewsDeps: Visualizing the Origin of Information in News Articles](https://mlsanity.com/paper/1909.10266/) (2019)

## Built on by (most cited first, 2 of 2)

- [Debating the Unspoken: Role-Anchored Multi-Agent Reasoning for Half-Truth Detection](https://mlsanity.com/paper/2604.19005/) (2026)
- [LASTIST: LArge-Scale Target-Independent STance dataset](https://mlsanity.com/paper/2510.25783/) (2025)

## Abstract (arXiv)

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.