  Background: The COVID-19 outbreak has left many people isolated within their
homes; these people are turning to social media for news and social connection,
which leaves them vulnerable to believing and sharing misinformation.
Health-related misinformation threatens adherence to public health messaging,
and monitoring its spread on social media is critical to understanding the
evolution of ideas that have potentially negative public health impacts.
Results: Analysis using model-labeled data was beneficial for increasing the
proportion of data matching misinformation indicators. Random forest classifier
metrics varied across the four conspiracy theories considered (F1 scores
between 0.347 and 0.857); this performance increased as the given conspiracy
theory was more narrowly defined. We showed that misinformation tweets
demonstrate more negative sentiment when compared to nonmisinformation tweets
and that theories evolve over time, incorporating details from unrelated
conspiracy theories as well as real-world events. Conclusions: Although we
focus here on health-related misinformation, this combination of approaches is
not specific to public health and is valuable for characterizing misinformation
in general, which is an important first step in creating targeted messaging to
counteract its spread. Initial messaging should aim to preempt generalized
misinformation before it becomes widespread, while later messaging will need to
target evolving conspiracy theories and the new facets of each as they become
incorporated.
