  With the explosive growth of the Coronavirus Pandemic (COVID-19),
misinformation on social media has developed into a global phenomenon with
widespread and detrimental societal effects. Despite recent progress and
efforts in detecting COVID-19 misinformation on social media networks, this
task remains challenging due to the complexity, diversity, multi-modality, and
high costs of fact-checking or annotation. In this research, we introduce a
systematic and multidisciplinary agent-based modeling approach to limit the
spread of COVID-19 misinformation and interpret the dynamic actions of users
and communities in evolutionary online (or offline) social media networks. Our
model was applied to a Twitter network associated with an armed protest
demonstration against the COVID-19 lockdown in Michigan state in May, 2020. We
implemented a one-median problem to categorize the Twitter network into six key
communities (nodes) and identified information exchange (links) within the
network. We measured the response time to COVID-19 misinformation spread in the
network and employed a cybernetic organizational method to monitor the Twitter
network. The overall misinformation mitigation strategy was evaluated, and
agents were allocated to interact with the network based on the measured
response time and feedback. The proposed model prioritized the communities
based on the agents response times at the operational level. It then optimized
agent allocation to limit the spread of COVID19 related misinformation from
different communities, improved the information diffusion delay threshold to up
to 3 minutes, and ultimately enhanced the mitigation process to reduce
misinformation spread across the entire network.
