Experiments in News Bias Detection with Pre-Trained Neural Transformers
This work addresses the issue of biased or fake information spread by state and commercial actors, which is an incremental contribution to news bias detection.
The researchers tackled the problem of detecting biased or fake news by comparing several pre-trained language models for sentence-level news bias detection and sub-type classification, providing quantitative and qualitative results.
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.