Original title: Combatting Human Trafficking in the Cyberspace: A Natural Language Processing-Based Methodology to Analyze the Language in Online Advertisements
Authors: Alejandro Rodriguez Perez, Pablo Rivas
The article addresses human trafficking in online marketplaces, using advanced Natural Language Processing (NLP). Their innovative method creates datasets for training NLP models with minimal supervision, crucial for tasks like Human Trafficking Risk Prediction (HTRP) and spotting Organized Activity. They employ cutting-edge Transformer models and introduce an interpretability framework for clear insights, aiding law enforcement. This work not only fills a significant gap in research but also offers a scalable, machine-learning approach to combat online human exploitation. It sets a foundation for future studies, highlighting how machine learning can tackle complex social issues effectively. Essentially, it’s a step forward in using technology to fight against human trafficking online, providing tools for better detection and prevention, ultimately aiding law enforcement and social welfare efforts.
Original article: https://arxiv.org/abs/2311.13118