Investigating Links between Illicit Massage Businesses through Natural Language Processing and Graph Machine Learning

Human trafficking exploits vulnerable individuals through forced sex or labor. Illicit massage businesses offer a clandestine front to illicit activities by disguising themselves as legitimate businesses. This makes it challenging for law enforcement agencies and anti-trafficking organizations to detect these enterprises and their associated entities, disrupt the network, and save victims. We adopt a multi-stream data integration approach primarily focusing on consumer-generated business reviews on Yelp.com, enriched with features from contextual data sources, such as the U.S. Census and business license records. We propose a novel decision support framework that extends the traditional link prediction methods by defining a higher-order neighborhood to detect links between pairs of massage businesses and the exposure of businesses to illicit activities related to human trafficking. We achieve this by introducing a bespoke subgraph extraction strategy in GNNs where the node features are derived using NLP techniques. Comprehensive experimental results demonstrate the competitive performance of our approach over the baseline methods.