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Problem definition: Approximately 11,000 alleged illicit massage businesses (IMBs) exist across the United States hidden in plain sight among legitimate businesses. These illicit businesses frequently exploit workers, many of whom are victims of human trafficking, forced or coerced to provide commercial sex. Academic/practical relevance: Although IMB review boards like Rubmaps.ch can provide first-hand information to identify IMBs, these sites are likely to be closed by law enforcement. Open websites like Yelp.com provide more accessible and detailed information about a larger set of massage businesses. Reviews from these sites can be screened for risk factors of trafficking. Methodology: We develop a natural language processing approach to detect online customer reviews that indicate a massage business is likely engaged in human trafficking. We label data sets of Yelp reviews using knowledge of known IMBs. We develop a lexicon of key words/phrases related to human trafficking and commercial sex acts. We then build two classification models based on this lexicon. We also train two classification models using embeddings from the bidirectional encoder representations from transformers (BERT) model and the Doc2Vec model. Results: We evaluate the performance of these classification models and various ensemble models. The lexicon-based models achieve high precision, whereas the embedding-based models have relatively high recall. The ensemble models provide a compromise and achieve the best performance on the out-of-sample test. Our results verify the usefulness of ensemble methods for building robust models to detect risk factors of human trafficking in reviews on open websites like Yelp. Managerial implications: The proposed models can save countless hours in IMB investigations by automatically sorting through large quantities of data to flag potential illicit activity, eliminating the need for manual screening of these reviews by law enforcement and other stakeholders. Funding: This work was supported by the National Science Foundation [Grant 1936331]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1196 .
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Non-traditional cyber adversaries: Combating human trafficking through data science
Human trafficking is a complex and challenging global crime exacerbated by the use of technology. Traffickers utilize technology for scalability, anonymity, and profitability as the Internet, social media platforms and encrypted messaging make the recruitment, exploitation and profit of an individual a low-risk, high-reward enterprise. Counter-trafficking efforts are often siloed approaches, resulting in decentralized information and analysis on the size and scope of trafficking in persons. Resources and tools such as the human trafficking kill chain methodology and Artemis, a machine learning (ML) human trafficking risk classifier, show promising disruption tactics which may also be applied to other asymmetrical threats. Recommendations for centralized data collections methods, inter-agency collaboration, and cybersecurity adjacent legislation are also made.
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- Award ID(s):
- 1936331
- PAR ID:
- 10191333
- Date Published:
- Journal Name:
- Cyber security
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2398-5119
- Page Range / eLocation ID:
- 1 - 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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