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New York City (NYC) experienced severe air pollution from Canadian wildfires in June 2023, disrupting travel and daily activities. This study analyzed public reactions to evacuation, indoor activities, shopping, and recreation using geotagged X posts during the air pollution crisis. Geotagged posts were reverse geocoded to census blocks and spatially joined with socioeconomic and demographic data from the U.S. census and American Community Survey. The dataset initially comprised 0.59 million geotagged X posts from 66,858 unique users in NYC over a 1-week period. After relevance filtering, the final dataset included 10,258 posts from 10,258 unique users on wildfire-related travel and activity discussions. Public reactions were analyzed using a BERT-based natural language processing model, whereas a gender–race model inferred users’ gender and racial identities based on their first and last names. A multinomial logit model assessed how socioeconomic and demographic factors influenced activity discussions during the crisis. The findings revealed demographic differences in responses. For instance, females were less likely to discuss evacuation and essential trips, possibly owing to continued workplace operations despite hazardous conditions. Racial differences were also evident, with Asians more frequently mentioning evacuation and commuting, whereas African Americans showed lower engagement in discussions about social and recreational activities. Socioeconomic disparities further influenced response patterns, as lower-income and less-educated groups expressed fewer concerns about evacuation, highlighting potential barriers to crisis awareness and preparedness. These insights emphasize the need for targeted communication strategies and equitable health interventions to ensure that emergency responses effectively reach vulnerable populations during environmental crises. 

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