Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset

TL;DR

This study introduces a new Twitter dataset and analysis methods to distinguish and characterize COVID-19 misinformation and informed communities, revealing their structural and linguistic differences.

Contribution

The paper provides a novel dataset and comprehensive analysis of COVID-19 misinformation communities, highlighting their network density, organization, and linguistic patterns.

Findings

Misinformed communities are denser and more organized.

A large portion of misinformed users may be anti-vaxxers.

Informed users tend to use more narratives.

Abstract

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.