  Understanding how different online communities engage with COVID-19
misinformation is critical for public health response, as 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 influential
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 a linked-media preference score, and
use a standardized similarity score to examine alignment of tweets within and
between communities. We find that media preference is strongly correlated with
the amount of misinformation propagated by sentinel nodes. Engagement with
sensationalist misinformation topics is largely confined to a cluster of
sentinel nodes that includes influential conspiracy theorist accounts, while
misinformation relating to COVID-19 severity generated widespread engagement
across multiple communities. Our findings indicate that misinformation
downplaying COVID-19 severity is of particular concern for public health
response.
