Abstract
Human trafficking is a global epidemic affecting millions of people across the planet. Sex trafficking, the dominant form of human trafficking, has seen a significant rise mostly due to the abundance of escort websites, where human traffickers can openly advertise among at-will escort advertisements. In this paper, we take a major step in the automatic detection of advertisements suspected to pertain to human trafficking. We present a novel dataset called Trafficking-10k, with more than 10,000 advertisements annotated for this task. The dataset contains two sources of information per advertisement: text and images. For the accurate detection of trafficking advertisements, we designed and trained a deep multimodal model called the Human Trafficking Deep Network (HTDN).- Anthology ID:
- P17-1142
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1547–1556
- Language:
- URL:
- https://aclanthology.org/P17-1142
- DOI:
- 10.18653/v1/P17-1142
- Cite (ACL):
- Edmund Tong, Amir Zadeh, Cara Jones, and Louis-Philippe Morency. 2017. Combating Human Trafficking with Multimodal Deep Models. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1547–1556, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Combating Human Trafficking with Multimodal Deep Models (Tong et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P17-1142.pdf