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, their ability to provide actionable insights in in part limited by their
lack of fidelity and generalizability to existing networks. 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.

我们开发了一个模拟框架，用于研究在线社交网络中的虚假信息传播，结合基于代理的建模和自然语言处理技术。