A network-based approach to QAnon user dynamics and topic diversity during the COVID-19 infodemic

TL;DR

This study analyzes Twitter user dynamics related to QAnon during the COVID-19 infodemic, revealing different population trends, the presence of bots, and the evolution of the conspiracy theory using a network-based method.

Contribution

It introduces a simple network-based approach to study the evolution and diversity of QAnon-related user behavior and topics during the COVID-19 infodemic.

Findings

Pro- and anti-QAnon users show different population dynamics.

Many QAnon clusters include bot users.

QAnon continues to evolve beyond its original scope.

Abstract

QAnon is an umbrella conspiracy theory that encompasses a wide spectrum of people. The COVID-19 pandemic has helped raise the QAnon conspiracy theory to a wide-spreading movement, especially in the US. Here, we study users' dynamics on Twitter related to the QAnon movement (i.e., pro-/anti-QAnon and less-leaning users) in the context of the COVID-19 infodemic and the topics involved using a simple network-based approach. We found that pro- and anti-leaning users show different population dynamics and that late less-leaning users were mostly anti-QAnon. These trends might have been affected by Twitter's suspension strategies. We also found that QAnon clusters include many bot users. Furthermore, our results suggest that QAnon continues to evolve amid the infodemic and does not limit itself to its original idea but instead extends its reach to create a much larger umbrella conspiracy theory. The network-based approach in this study is important for nowcasting the evolution of the QAnon movement.