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The COVID-19 pandemic has had a profound impact on the global community, and vaccination has been recognized as a crucial intervention. To gain insight into public perceptions of COVID-19 vaccines, survey studies and the analysis of social media platforms have been conducted. However, existing methods lack consideration of individual vaccination intentions or status and the relationship between public perceptions and actual vaccine uptake. To address these limitations, this study proposes a text classification approach to identify tweets indicating a user’s intent or status on vaccination. A comparative analysis between the proportions of tweets from different categories and real-world vaccination data reveals notable alignment, suggesting that tweets may serve as a precursor to actual vaccination status. Further, regression analysis and time series forecasting were performed to explore the potential of tweet data, demonstrating the significance of incorporating tweet data in predicting future vaccination status. Finally, clustering was applied to the tweet sets with positive and negative labels to gain insights into underlying focuses of each stance. 

Evacuations have a significant impact on saving human lives during hurricanes. However, as a complex dynamic process, it is typically difficult to know individual evacuation decisions in real-time. Since a large amount of information is continuously posted through social media platforms, we can use them to understand individual evacuation behavior. In this paper, we collect tweets during Hurricane Irma in 2017 and train a text classifier in an active learning way to distinguish tweets expressing positive evacuation decisions from both negative and irrelevant ones. Additionally, we perform a demographic analysis and content clustering to investigate the potential causes and correlates of evacuation decisions. The results can be used to help inform planning strategies of emergency response agencies.