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.

通过先进的自然语言处理技术，本项目解决在线 C2C 市场上的人口贩运问题。我们介绍了一种新的方法论，通过最小的监督生成伪标记数据集，为训练最先进的自然语言处理模型提供了丰富的资源。在人口贩卖风险预测和有组织活动检测等任务中，我们采用了先进的 Transformer 模型进行分析。一个重要的贡献是使用整合梯度实现一个可解释性框架，为执法部门提供关键的可解释性洞察。这项工作不仅填补了文献中的一个重要空白，还提供了一种可扩展的、机器学习驱动的在线人类剥削对策方法。它为未来的研究和实际应用奠定了基础，强调了机器学习在解决复杂社会问题中的作用。