Evaluation of Fake News Detection with Knowledge-Enhanced Language Models
This work addresses fake news detection for social media and political contexts, but it is incremental as it evaluates existing knowledge integration methods rather than proposing new ones.
The paper tackled fake news detection by integrating knowledge bases into pre-trained language models, finding that knowledge-enhanced models significantly improved performance on the LIAR dataset but had mixed results on COVID-19 due to reliance on stylistic features and outdated knowledge.
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.