@inproceedings{garg-etal-2026-investigating,
title = "Investigating Links between Illicit Massage Businesses through Natural Language Processing and Graph Machine Learning",
author = "Garg, Vasuki and
Ozaltin, Osman and
Mayorga, Maria and
Caltagirone, Sherrie",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1702/",
doi = "10.18653/v1/2026.findings-acl.1702",
pages = "34080--34096",
ISBN = "979-8-89176-395-1",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Investigating Links between Illicit Massage Businesses through Natural Language Processing and Graph Machine Learning
%A Garg, Vasuki
%A Ozaltin, Osman
%A Mayorga, Maria
%A Caltagirone, Sherrie
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F garg-etal-2026-investigating
%X 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.
%R 10.18653/v1/2026.findings-acl.1702
%U https://aclanthology.org/2026.findings-acl.1702/
%U https://doi.org/10.18653/v1/2026.findings-acl.1702
%P 34080-34096
Markdown (Informal)
[Investigating Links between Illicit Massage Businesses through Natural Language Processing and Graph Machine Learning](https://aclanthology.org/2026.findings-acl.1702/) (Garg et al., Findings 2026)
ACL