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Uncovering Patterns of Violence in Mexican Digital News Articles Through Data Science Methods

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Publicado en:International Journal of Combinatorial Optimization Problems and Informatics vol. 16, no. 3 (2025), p. 195-216
Autor principal: Zárate-Cartas, Jonathan
Otros Autores: Molina-Villegas, Alejandro
Publicado:
International Journal of Combinatorial Optimization Problems & Informatics
Materias:
Violence
Metropolitan areas
Spatial analysis
Deep learning
Artificial intelligence
Texts
Data science
Unsupervised learning
Tools
Gender-based violence
Crime
Domestic violence
Newspapers
Optimization
Neural networks
Femicide
Natural language processing
Women
Informatics
Automatic text analysis
Murders & murder attempts
Human rights
Acceso en línea:Citation/Abstract
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Resumen:Violence against women is one of the most common human rights violations, with its most extreme form being femicide. In this context, we considered it relevant to demonstrate how artificial intelligence tools and geospatial analysis techniques can contribute to a better and faster analysis of these crimes. In this study, we analysed femicides that occurred in Mexico between 2014 and 2022. Our data source comprised digital news articles from leading Mexican newspapers. The study begins with the preprocessing of texts and the detection of those mentioning femicide. Subsequently, using unsupervised learning models, we grouped the texts according to their semantic similarity. We then employed deep learning models to classify each crime according to its specific characteristics. Finally, we used spatial analysis tools to detect geographic patterns in the occurrence of these crimes in the metropolitan area of the Valley of Mexico, analysing the automatically detected characteristics as variables.
ISSN:2007-1558
DOI:10.61467/2007.1558.2025.v16i3.602
Fuente:Advanced Technologies & Aerospace Database