Combatting Human Trafficking in the Cyberspace: A Natural Language
  Processing-Based Methodology to Analyze the Language in Online Advertisements
Alejandro Rodriguez Perez Pablo Rivas
This project tackles the pressing issue of human trafficking in online C2C
marketplaces through advanced Natural Language Processing (NLP) techniques. We
introduce a novel methodology for generating pseudo-labeled datasets with
minimal supervision, serving as a rich resource for training state-of-the-art
NLP models. Focusing on tasks like Human Trafficking Risk Prediction (HTRP) and
Organized Activity Detection (OAD), we employ cutting-edge Transformer models
for analysis. A key contribution is the implementation of an interpretability
framework using Integrated Gradients, providing explainable insights crucial
for law enforcement. This work not only fills a critical gap in the literature
but also offers a scalable, machine learning-driven approach to combat human
exploitation online. It serves as a foundation for future research and
practical applications, emphasizing the role of machine learning in addressing
complex social issues.