# A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation

arXiv: https://arxiv.org/abs/2304.04811 · HTML: https://mlsanity.com/paper/2304.04811/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset](https://mlsanity.com/paper/2008.00791/) (2020)
- [The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic](https://mlsanity.com/paper/2011.05773/) (2020)
- [VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter](https://mlsanity.com/paper/2301.06660/) (2023)
- [A first look at COVID-19 information and misinformation sharing on Twitter](https://mlsanity.com/paper/2003.13907/) (2020)
- [No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection](https://mlsanity.com/paper/2010.06906/) (2020)
- [Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic](https://mlsanity.com/paper/2106.11702/) (2021)
- [Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19](https://mlsanity.com/paper/2005.06012/) (2020)
- [Drink Bleach or Do What Now? Covid-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation](https://mlsanity.com/paper/2010.08743/) (2020)
- [Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media](https://mlsanity.com/paper/2204.10080/) (2022)
- [CML-COVID: A Large-Scale COVID-19 Twitter Dataset with Latent Topics, Sentiment and Location Information](https://mlsanity.com/paper/2101.12202/) (2021)
- [The State of Infodemic on Twitter](https://mlsanity.com/paper/2105.07730/) (2021)

## Abstract (arXiv)

The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challenging for citizens to differentiate between accurate and inaccurate information about COVID-19. This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets. The study makes comparisons alongside four key aspects: 1) the distribution of topics, 2) the live status of tweets, 3) language analysis and 4) the spreading power over time. An added contribution of this study is the creation of a COVID-19 misinformation classification dataset. Finally, we demonstrate that this new dataset helps improve misinformation classification by more than 9% based on average F1 measure.