  Recent advances in fake news detection have exploited the success of
large-scale pre-trained language models (PLMs). The predominant
state-of-the-art approaches are based on fine-tuning PLMs on labelled fake news
datasets. However, large-scale PLMs are generally not trained on structured
factual data and hence may not possess priors that are grounded in factually
accurate knowledge. The use of existing knowledge bases (KBs) with rich
human-curated factual information has thus the potential to make fake news
detection more effective and robust. In this paper, we investigate the impact
of knowledge integration into PLMs for fake news detection. We study several
state-of-the-art approaches for knowledge integration, mostly using Wikidata as
KB, on two popular fake news datasets - LIAR, a politics-based dataset, and
COVID-19, a dataset of messages posted on social media relating to the COVID-19
pandemic. Our experiments show that knowledge-enhanced models can significantly
improve fake news detection on LIAR where the KB is relevant and up-to-date.
The mixed results on COVID-19 highlight the reliance on stylistic features and
the importance of domain-specific and current KBs.
