Improved Models for Media Bias Detection and Subcategorization
Not on the map. Why?
Abstract (arXiv)
We present improved models for the granular detection and sub-classification news media bias in English news articles. We compare the performance of zero-shot versus fine-tuned large pre-trained neural transformer language models, explore how the level of detail of the classes affects performance on a novel taxonomy of 27 news bias-types, and demonstrate how using synthetically generated example data can be used to improve quality
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 8 of its 43 references that are in the map. First posted on arXiv 16 Dec 2024.
Builds on
8 of 43 references.
References · 8
- 2017Attention Is All You Needcited by 60,106
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
- 2019Fine-Grained Analysis of Propaganda in News Articlescited by 130
- 2020We Can Detect Your Bias: Predicting the Political Ideology of News Articlescited by 92
- 2019In Plain Sight: Media Bias Through the Lens of Factual Reportingcited by 68
- 2022Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Expertscited by 55
- 2021MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristicscited by 24
- 2023Developing a Natural Language Understanding Model to Characterize Cable News Biascited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.