A methodology to infer value networks from police case files

Abstract

Every year, hundreds of case files from police investigations and court cases are produced. The challenge of manual processing and analysing the vast volume of documentation resulting from investigations have prompted law enforcement agencies to embrace Natural Language Processing (NLP). Despite this, a systematic approach to automatically retrieve relevant data from police case files is lacking. To address this gap, we propose a methodology to process police case files and perform criminals’ role classification, criminals’ ties classification, and value network inferring through syntactic pattern recognition and BERTopic. The results reached allow us to affirm that the inferred value networks can congruently depict the roles and the connections between them. Although further research is needed, this methodology can propel the automation of the extraction of criminal networks’ dynamics implemented by law enforcement agencies, allowing them to refine and improve the strategies employed to disrupt criminal networks.