Year: 2025
Type: article
Abstract: The emergence of social media offers unprecedented opportunities to map social unrest with high spatiotemporal resolution. This study leverages geolocated social media footage to analyze the spatiotemporal distribution of the 2023 ‘Nahel Merzouk’ riots in France. Using a fine-tuned computer vision model, we detect riot-related content in visual data and validate our approach by comparing the spatiotemporal patterns of detected posts with rioting events reported in the press. Our method yields a spatial resolution of 300 $$\times$$ 300 m, thereby facilitating a detailed analysis of riot distributions at unprecedented scale. By applying density-based clustering, we map riots across seven French cities, revealing their highly localized and bursty dynamics. This study opens pathways for future research on the causes and dynamics of social unrest, enabling a deeper understanding of urban riots and their potential mitigation.
Source: Journal Of Big Data
Institution: Delft University of Technology
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,290
Funder: Technische Universiteit Delft
OpenAlex ID: W4412660269