  It has been argued that fake news and the spread of false information pose a
threat to societies throughout the world, from influencing the results of
elections to hindering the efforts to manage the COVID-19 pandemic. To combat
this threat, a number of Natural Language Processing (NLP) approaches have been
developed. These leverage a number of datasets, feature extraction/selection
techniques and machine learning (ML) algorithms to detect fake news before it
spreads. While these methods are well-documented, there is less evidence
regarding their efficacy in this domain. By systematically reviewing the
literature, this paper aims to delineate the approaches for fake news detection
that are most performant, identify limitations with existing approaches, and
suggest ways these can be mitigated. The analysis of the results indicates that
Ensemble Methods using a combination of news content and socially-based
features are currently the most effective. Finally, it is proposed that future
research should focus on developing approaches that address generalisability
issues (which, in part, arise from limitations with current datasets),
explainability and bias.
