Detecting Human Trafficking: Automated Classification of Online Customer Reviews of Massage Businesses

Ruoting Li (), Margaret Tobey (), Maria E. Mayorga (), Sherrie Caltagirone () and Osman Y. Özaltın ()
Additional contact information
Ruoting Li: Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina 27695
Margaret Tobey: Operations Research Graduate Program, North Carolina State University, Raleigh, North Carolina 27695
Maria E. Mayorga: Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina 27695
Sherrie Caltagirone: Global Emancipation Network, Clermont, Florida 34715
Osman Y. Özaltın: Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina 27695

Manufacturing & Service Operations Management, 2023, vol. 25, issue 3, 1051-1065

Abstract: 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.

Keywords: human trafficking; massage businesses; online customer reviews; Natural Language Processing; ensemble learning (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2023.1196 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:25:y:2023:i:3:p:1051-1065

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().