Experiments in News Bias Detection with Pre-Trained Neural Transformers

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. Our findings are to be seen as part of a wider effort towards realizing the conceptual vision, articulated by Fuhr et al. [10], of a "nutrition label" for online content for the social good.