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

ACL ARR 2026 January Submission10495 Authors

Keywords: Natural Language Processing, Graph Machine Learning, Link Prediction, Human Trafficking, Illicit Massage Business

Abstract: 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.

Paper Type: Long

Research Area: Computational Social Science, Cultural Analytics, and NLP for Social Good

Research Area Keywords: NLP tools for social analysis, quantitative analyses of news and/or social media

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data analysis

Languages Studied: English

Submission Number: 10495