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

Abstract

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.

arXiv comment43 pages, 6 figures

References in corpus (21)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• The science of fake news
• Estimating Continuous Distributions in Bayesian Classifiers
• BERTScore: Evaluating Text Generation with BERT
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Cited by in corpus (4)

• A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data
• COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web Application for Classifying COVID-19 Discussions
• Analysis of child development facts and myths using text mining techniques and classification models
• Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation