Combatting Human Trafficking in the Cyberspace: A Natural Language Processing-Based Methodology to Analyze the Language in Online Advertisements
This addresses the problem of detecting human trafficking in online advertisements for law enforcement and social services, representing a domain-specific application with incremental methodological improvements.
This project tackles human trafficking in online marketplaces by developing a novel NLP methodology for generating pseudo-labeled datasets with minimal supervision, achieving state-of-the-art performance in tasks like Human Trafficking Risk Prediction and Organized Activity Detection using Transformer models.
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.