Understanding the spatial and temporal impact of global events through large-scale social media data

Abstract Large-scale urban social media data can provide substantial insights into the real-time development of cities around the globe, illuminating phenomena such as gentrification, urban decay, and resilience to major adverse events. This study utilizes a dataset of over 147.8 million georeferenced tweets from multiple cities to demonstrate their potential for analyzing the emotional and temporal impacts of major events, including the U.S. presidential elections and the Covid-19 pandemic. By employing a sentiment indicator and an anxiety indicator, we highlight the importance of establishing robust baselines that are not only city-specific but also long-term, population-based, and user-based. We demonstrate the value of integrating georeferenced data with long-term analysis to uncover spatial and temporal patterns in public emotional responses, offering new perspectives on the dynamics of crises, such as climate change, and societal resilience.