# A Systematic Review on the Detection of Fake News Articles

arXiv: https://arxiv.org/abs/2110.11240 · HTML: https://mlsanity.com/paper/2110.11240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 70 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [A Survey on Natural Language Processing for Fake News Detection](https://mlsanity.com/paper/1811.00770/) (2018)
- [Combating Fake News: A Survey on Identification and Mitigation Techniques](https://mlsanity.com/paper/1901.06437/) (2019)
- [False News On Social Media: A Data-Driven Survey](https://mlsanity.com/paper/1902.07539/) (2019)
- [The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans](https://mlsanity.com/paper/1804.03461/) (2018)
- [SGG: Spinbot, Grammarly and GloVe based Fake News Detection](https://mlsanity.com/paper/2008.06854/) (2020)
- [Check-It: A Plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web](https://mlsanity.com/paper/1905.04260/) (2019)

## Abstract (arXiv)

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.