The rapid spread of COVID-19 misinformation on social media poses challenges in detection and analysis. There has been extensive discussion about the roles of online and offline campaigns in spreading misinformation. Recognizing the analytical gap between online and offline behaviors during the COVID-19 pandemic, we propose a systematic and multidisciplinary approach. This approach utilizes agent-based modeling to interpret the spread of misinformation and the actions of users/communities on social media networks. Our model was tested on a Twitter network concerning a demonstration against COVID-19 lockdowns in Michigan in May 2020. We implemented the one-median problem to categorize and simplify the Twitter network, measured the response time to the spread of misinformation, employed a cybernetic organizational method to manage the process of mitigating misinformation spread in the network, and optimized the allocation of agents to reduce the response time to misinformation spread. The study demonstrates the effectiveness of our proposed approach in delaying information diffusion, thereby mitigating the spread of COVID-19 misinformation on social media.