Sentinel node approach to monitoring online COVID-19 misinformation.

ABSTRACT: Understanding how different online communities engage with COVID-19 misinformation is critical for public health response. For example, misinformation confined to a small, isolated community of users poses a different public health risk than misinformation being consumed by a large population spanning many diverse communities. Here we take a longitudinal approach that leverages tools from network science to study COVID-19 misinformation on Twitter. Our approach provides a means to examine the breadth of misinformation engagement using modest data needs and computational resources. We identify a subset of accounts from different Twitter communities discussing COVID-19, and follow these 'sentinel nodes' longitudinally from July 2020 to January 2021. We characterize sentinel nodes in terms of

SUBMITTER: Osborne MT 

PROVIDER: S-EPMC9194351 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature