# Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset

arXiv: https://arxiv.org/abs/2008.00791 · HTML: https://mlsanity.com/paper/2008.00791/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 30 of 32)

- [Transformer based Automatic COVID-19 Fake News Detection System](https://mlsanity.com/paper/2101.00180/) (2021)
- [MMCoVaR: Multimodal COVID-19 Vaccine Focused Data Repository for Fake News Detection and a Baseline Architecture for Classification](https://mlsanity.com/paper/2109.06416/) (2021)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection](https://mlsanity.com/paper/2111.07819/) (2021)
- [A Guide to Misinformation Detection Data and Evaluation](https://mlsanity.com/paper/2411.05060/) (2024)
- [Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo](https://mlsanity.com/paper/2205.03068/) (2022)
- [COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives](https://mlsanity.com/paper/2106.08423/) (2021)
- [MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detection](https://mlsanity.com/paper/2205.09817/) (2022)
- [PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence](https://mlsanity.com/paper/2207.13970/) (2022)
- [Codes, Patterns and Shapes of Contemporary Online Antisemitism and Conspiracy Narratives -- an Annotation Guide and Labeled German-Language Dataset in the Context of COVID-19](https://mlsanity.com/paper/2210.07934/) (2022)
- [Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims](https://mlsanity.com/paper/2205.02596/) (2022)
- [Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement](https://mlsanity.com/paper/2202.12413/) (2022)
- [The COVMis-Stance dataset: Stance Detection on Twitter for COVID-19 Misinformation](https://mlsanity.com/paper/2204.02000/) (2022)
- [MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection](https://mlsanity.com/paper/2210.05401/) (2022)
- [Detection of Conspiracy Theories Beyond Keyword Bias in German-Language Telegram Using Large Language Models](https://mlsanity.com/paper/2404.17985/) (2024)
- [Evaluating Generalizability of Fine-Tuned Models for Fake News Detection](https://mlsanity.com/paper/2205.07154/) (2022)
- [Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling](https://mlsanity.com/paper/2310.07078/) (2023)
- [What distinguishes conspiracy from critical narratives? A computational analysis of oppositional discourse](https://mlsanity.com/paper/2407.10745/) (2024)
- [Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning](https://mlsanity.com/paper/2110.15703/) (2021)
- [Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives](https://mlsanity.com/paper/2107.09768/) (2021)
- [Political Communities on Twitter: Case Study of the 2022 French Presidential Election](https://mlsanity.com/paper/2204.07436/) (2022)
- [X-CapsNet For Fake News Detection](https://mlsanity.com/paper/2307.12332/) (2023)
- [BotSim: Mitigating The Formation Of Conspiratorial Societies with Useful Bots](https://mlsanity.com/paper/2601.06154/) (2026)
- [Misinformation detection in Luganda-English code-mixed social media text](https://mlsanity.com/paper/2104.00124/) (2021)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)
- [A Weakly-Supervised Iterative Graph-Based Approach to Retrieve COVID-19 Misinformation Topics](https://mlsanity.com/paper/2205.09416/) (2022)
- ["COVID-19 was a FIFA conspiracy #curropt": An Investigation into the Viral Spread of COVID-19 Misinformation](https://mlsanity.com/paper/2207.01483/) (2022)
- [A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation](https://mlsanity.com/paper/2304.04811/) (2023)
- [Characterizing the Emotion Carriers of COVID-19 Misinformation and Their Impact on Vaccination Outcomes in India and the United States](https://mlsanity.com/paper/2306.13954/) (2023)
- [The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset](https://mlsanity.com/paper/2308.15154/) (2023)

## Abstract (arXiv)

From conspiracy theories to fake cures and fake treatments, COVID-19 has become a hot-bed for the spread of misinformation online. It is more important than ever to identify methods to debunk and correct false information online. In this paper, we present a methodology and analyses to characterize the two competing COVID-19 misinformation communities online: (i) misinformed users or users who are actively posting misinformation, and (ii) informed users or users who are actively spreading true information, or calling out misinformation. The goals of this study are two-fold: (i) collecting a diverse set of annotated COVID-19 Twitter dataset that can be used by the research community to conduct meaningful analysis; and (ii) characterizing the two target communities in terms of their network structure, linguistic patterns, and their membership in other communities. Our analyses show that COVID-19 misinformed communities are denser, and more organized than informed communities, with a possibility of a high volume of the misinformation being part of disinformation campaigns. Our analyses also suggest that a large majority of misinformed users may be anti-vaxxers. Finally, our sociolinguistic analyses suggest that COVID-19 informed users tend to use more narratives than misinformed users.