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In an era increasingly affected by natural and human-caused disasters, the role of social media in disaster communication has become ever more critical. Despite substantial research on social media use during crises, a significant gap remains in detecting crisis-related misinformation. Detecting deviations in information is fundamental for identifying and curbing the spread of misinformation. This study introduces a novel Information Switching Pattern Model to identify dynamic shifts in perspectives among users who mention each other in crisisrelated narratives on social media. These shifts serve as evidence of crisis misinformation affecting user-mention network interactions. The study utilizes advanced natural language processing, network science, and census data to analyze geotagged tweets related to compound disaster events in Oklahoma in 2022. The impact of misinformation is revealed by distinct engagement patterns among various user types, such as bots, private organizations, non-profits, government agencies, and news media throughout different disaster stages. These patterns show how different disasters influence public sentiment, highlight the heightened vulnerability of mobile home communities, and underscore the importance of education and transportation access in crisis response. Understanding these engagement patterns is crucial for detecting misinformation and leveraging social media as an effective tool for risk communication during disasters