# CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets using Deep Learning Models

arXiv: https://arxiv.org/abs/2209.05667 · HTML: https://mlsanity.com/paper/2209.05667/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [RMDL: Random Multimodel Deep Learning for Classification](https://mlsanity.com/paper/1805.01890/) (2018)

## Built on by (most cited first, 2 of 2)

- [A Survey of Social Cybersecurity: Techniques for Attack Detection, Evaluations, Challenges, and Future Prospects](https://mlsanity.com/paper/2504.04311/) (2025)
- [Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media posts](https://mlsanity.com/paper/2608.09510/) (2026)

## Abstract (arXiv)

Online news and information sources are convenient and accessible ways to learn about current issues. For instance, more than 300 million people engage with posts on Twitter globally, which provides the possibility to disseminate misleading information. There are numerous cases where violent crimes have been committed due to fake news. This research presents the CovidMis20 dataset (COVID-19 Misinformation 2020 dataset), which consists of 1,375,592 tweets collected from February to July 2020. CovidMis20 can be automatically updated to fetch the latest news and is publicly available at: https://github.com/everythingguy/CovidMis20. This research was conducted using Bi-LSTM deep learning and an ensemble CNN+Bi-GRU for fake news detection. The results showed that, with testing accuracy of 92.23% and 90.56%, respectively, the ensemble CNN+Bi-GRU model consistently provided higher accuracy than the Bi-LSTM model.