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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 

Stakeholder engagement is a vital, yet under-accessed and under-studied, resource for tackling wicked environmental problems. This paper examines mental models – cognitive representations of “real” systems – refined through group model building (GMB) with stakeholders concerned with environmental issues in the US state of Oklahoma. During GMB, a diverse group of high-level decision makers met with a variety of physical and social scientists to collaborate on mental models concerning three environmental focus areas in Oklahoma: encroachment of eastern red cedar, grid and infrastructure resilience, and marginal water use and re-use. We ask: how do individuals from diverse stakeholder groups describe causes and consequences of key environmental problems in Oklahoma? Results from this analysis advance efforts toward developing socially sustainable solutions for environmental problems in Oklahoma and beyond. 

Utilizing a random sample of Oklahoma, USA residents, this paper examines the factors that are 1) associated with concern for the current state of the electrical grid in Oklahoma, and 2) associated with willingness-to-pay (WTP) for electrical grid improvements in the state. We develop a conceptual model using a risk perception framework and based on previous literature to hypothesize which variables should be related to our dependent variables (concern for the electrical grid, and WTP for improvements to the grid). We then test our conceptual model using a structural equation model (SEM). The results suggest that respondents who hold higher perceptions of weather-related risks and perceive more risks from electrical outages had greater concern for electricity infrastructure. Additionally, respondents who expressed less trust in those charged with electrical grid maintenance reported more concern for electricity infrastructure. The results for our second research question suggest that lower cost, respondents who were more politically liberal, non-white, trust grid maintenance, perceived risks of electrical outages and have concerns for the electrical grid infrastructure were all related to WTP for electrical grid improvements. We conclude the paper with implications of our findings and some brief recommendations for electrical grid concern and WTP for modernization.