Experiments in News Bias Detection with Pre-Trained Neural Transformers
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Abstract (arXiv)
The World Wide Web provides unrivalled access to information globally, including factual news reporting and commentary. However, state actors and commercial players increasingly spread biased (distorted) or fake (non-factual) information to promote their agendas. We compare several large, pre-trained language models on the task of sentence-level news bias detection and sub-type classification, providing quantitative and qualitative results.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 33 references that are in the map. First posted on arXiv 14 Jun 2024.
Builds on
8 of 33 references.
References · 8
- 2021MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristicscited by 24
- 2018Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media Landscapecited by 20
- 2021FakeFlow: Fake News Detection by Modeling the Flow of Affective Informationcited by 20
- 2020Detecting Media Bias in News Articles using Gaussian Bias Distributionscited by 19
- 2023Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collectioncited by 17
- 2021Towards Explainable Fact Checkingcited by 9
- 2020Investigating Differences in Crowdsourced News Credibility Assessment: Raters, Tasks, and Expert Criteriacited by 8
- 2021Newsalyze: Enabling News Consumers to Understand Media Biascited by 4
Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.