# A Survey of COVID-19 Misinformation: Datasets, Detection Techniques and Open Issues

arXiv: https://arxiv.org/abs/2110.00737 · HTML: https://mlsanity.com/paper/2110.00737/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2021.

## Built on by (most cited first, 6 of 6)

- [Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media](https://mlsanity.com/paper/2210.07518/) (2022)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [COVIDFakeExplainer: An Explainable Machine Learning based Web Application for Detecting COVID-19 Fake News](https://mlsanity.com/paper/2310.13890/) (2023)
- [A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data](https://mlsanity.com/paper/2311.08001/) (2023)
- [COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web Application for Classifying COVID-19 Discussions](https://mlsanity.com/paper/2402.09897/) (2024)
- [Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation](https://mlsanity.com/paper/2604.09635/) (2026)

## Abstract (arXiv)

Misinformation during pandemic situations like COVID-19 is growing rapidly on social media and other platforms. This expeditious growth of misinformation creates adverse effects on the people living in the society. Researchers are trying their best to mitigate this problem using different approaches based on Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). This survey aims to study different approaches of misinformation detection on COVID-19 in recent literature to help the researchers in this domain. More specifically, we review the different methods used for COVID-19 misinformation detection in their research with an overview of data pre-processing and feature extraction methods to get a better understanding of their work. We also summarize the existing datasets which can be used for further research. Finally, we discuss the limitations of the existing methods and highlight some potential future research directions along this dimension to combat the spreading of misinformation during a pandemic.