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Spatiotemporal dynamics of violence in social unrest events based on geo-social media data

Author

Listed:
  • Huanying Chen
  • Xintao Liu
  • Songnian Li
  • Asher Yair Grinberger
  • Hangbin Wu
  • Chun Liu
  • Wei Huang

Abstract

Unrest events occur in the society in various forms. Some of them can escalate into violence, causing severe damage to property, individuals, and society. Recently, social media, together with AI, has become a crucial tool for monitoring and investigating the development and impact of unrest events. This research aims to comprehensively analyze social media data to explore the spatiotemporal patterns of activities and violence associated with unrest events. Highly relevant tweets expressing negative emotions are extracted and analyzed using AI-based natural language processing (NLP) to capture violence within the unrest. Additionally, a data analytics workflow that integrates temporal, spatial, semantic, and network-based methods is employed to provide a comprehensive exploration of the unrest. Using the 2013 Brazil Protests as a case study, we divide the unrest into 5 phases based on the frequency and spatial distribution of negative tweets. During these phases, both the frequency and spatial scope increase, peaking in the 3rd phase before gradually declining. We apply Biterm Topic Modeling (BTM) to identify public concerns during the unrest and analyze their temporal and spatial dynamics. The results reveal that violence is most intense in the 3rd phase and is primarily distributed across southeastern Brazil. Moreover, the spatiotemporal distribution of emotions indicates that fear and anger are the dominant negative emotions when violence occurs, contributing to significant escalations of the unrest. By constructing semantic-temporal networks of negative emotion flows, we pinpoint the leading cities in the unrest. Differences in topic flows within these networks suggest differing motivations behind the unrest, leading to the conclusion that locations where groups expressing concern about violence gather are more likely to become sites of actual violence. These findings can help the government formulate effective measures for managing unrest events and maintaining social stability.

Suggested Citation

  • Huanying Chen & Xintao Liu & Songnian Li & Asher Yair Grinberger & Hangbin Wu & Chun Liu & Wei Huang, 2026. "Spatiotemporal dynamics of violence in social unrest events based on geo-social media data," Environment and Planning B, , vol. 53(2), pages 303-318, February.
    • Handle: RePEc:sae:envirb:v:53:y:2026:i:2:p:303-318
    DOI: 10.1177/23998083251344354
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    References listed on IDEAS

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    1. Raquel Recuero & Gabriela Zago & Marco T. Bastos & Ricardo Araújo, 2015. "Hashtags Functions in the Protests Across Brazil," SAGE Open, , vol. 5(2), pages 21582440155, May.
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    4. Bakerman, Jordan & Pazdernik, Karl & Korkmaz, Gizem & Wilson, Alyson G., 2022. "Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest," International Journal of Forecasting, Elsevier, vol. 38(2), pages 648-661.
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