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Crowd Detection in Mass Gatherings Based on Social Media Data: A Case Study of the 2014 Shanghai New Year’s Eve Stampede

Author

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  • Jiexiong Duan

    (School of Earth and Space Sciences, Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871, China)

  • Weixin Zhai

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
    Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China)

  • Chengqi Cheng

    (College of Engineering, Peking University, Beijing 100871, China)

Abstract

The Shanghai New Year’s Eve stampede on 31 December 2014, caused 36 deaths and 47 other injuries, generating attention from around the world. This research aims to explore crowd aggregation from the perspective of Sina Weibo check-in data and evaluate the potential of crowd detection based on social media data. We develop a framework using Weibo check-in data in three dimensions: the aggregation level of check-in data, the topic changes in posts and the sentiment fluctuations of citizens. The results show that the numbers of check-ins in all of Shanghai on New Years’ Eve is twice that of other days and that Moran’s I reaches a peak on this date, implying a spatial autocorrelation mode. Additionally, the results of topic modeling indicate that 72.4% of the posts were related to the stampede, reflecting public attitudes and views on this incident from multiple angles. Moreover, sentiment analysis based on Weibo posts illustrates that the proportion of negative posts increased both when the stampede occurred (40.95%) and a few hours afterwards (44.33%). This study demonstrates the potential of using geotagged social media data to analyze population spatiotemporal activities, especially in emergencies.

Suggested Citation

  • Jiexiong Duan & Weixin Zhai & Chengqi Cheng, 2020. "Crowd Detection in Mass Gatherings Based on Social Media Data: A Case Study of the 2014 Shanghai New Year’s Eve Stampede," IJERPH, MDPI, vol. 17(22), pages 1-14, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:22:p:8640-:d:448544
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    2. Sarah Shtaierman & Catarina Fontes & Christoph Lütge, 2025. "The Potentials and Limitations of Agent-Based Models for Urban Digital Twins: Insights From a Surveillance and Behavioral Nudging Simulation," Urban Planning, Cogitatio Press, vol. 10.

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