  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.
