Combating Human Trafficking with Multimodal Deep Models

Edmund Tong, Amir Zadeh, Cara Jones, Louis-Philippe Morency


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P17-1142.pdf