Improved Models for Media Bias Detection and Subcategorization
arXiv:2412.11835 · doi:10.1007/978-3-031-70239-6_13
Abstract
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
References in corpus (5)
- Fine-Grained Analysis of Propaganda in News Articles
- Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts
- Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection
- MBIC -- A Media Bias Annotation Dataset Including Annotator Characteristics
- Experiments in News Bias Detection with Pre-Trained Neural Transformers