  We develop a simulation framework for studying misinformation spread within
online social networks that blends agent-based modeling and natural language
processing techniques. While many other agent-based simulations exist in this
space, questions over their fidelity and generalization to existing networks in
part hinders their ability to provide actionable insights. To partially address
these concerns, we create a 'digital clone' of a known misinformation sharing
network by downloading social media histories for over ten thousand of its
users. We parse these histories to both extract the structure of the network
and model the nuanced ways in which information is shared and spread among its
members. Unlike many other agent-based methods in this space, information
sharing between users in our framework is sensitive to topic of discussion,
user preferences, and online community dynamics. To evaluate the fidelity of
our method, we seed our cloned network with a set of posts recorded in the base
network and compare propagation dynamics between the two, observing reasonable
agreement across the twin networks over a variety of metrics. Lastly, we
explore how the cloned network may serve as a flexible, low-cost testbed for
misinformation countermeasure evaluation and red teaming analysis. We hope the
tools explored here augment existing efforts in the space and unlock new
opportunities for misinformation countermeasure evaluation, a field that may
become increasingly important to consider with the anticipated rise of
misinformation campaigns fueled by generative artificial intelligence.
