Work

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

Year: 2026

Type: article

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.

Source: EPJ Data Science

DOI: 10.1140/epjds/s13688-026-00657-y

Authors: Ann-Kathrin Meyer, Tobias Brandt

Institution: University of Münster

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,890