Enhancing the Detection of Criminal Organizations in Mexico using ML and NLP
This paper relies on Machine Learning (ML) and supervised Natural Language Processing (NLP) to generate a geo referenced database on the violent presence of Mexican Criminal Organizations (MCOs) between 2000 2018. This application responds to the need for high quality data on criminal groups to inform academic and policy analysis in a context of intense violence such as Mexico. Powered by ML and NLP tools, this computational social science application processes a vast collection of news stories written in Spanish to track MCOs' violent presence. The unprecedented granularity of the data allows disaggregating daily municipal information for 10 main MCOs comprising more than 200 specific criminal cells.
2020 International Joint Conference on Neural Networks (IJCNN)