Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements

Saeideh Shahrokh Esfahani, Michael J. Cafarella, Maziyar Baran Pouyan, Gregory DeAngelo, Elena Eneva, Andy E. Fano


Abstract
Human trafficking is a worldwide crisis. Traffickers exploit their victims by anonymously offering sexual services through online advertisements. These ads often contain clues that law enforcement can use to separate out potential trafficking cases from volunteer sex advertisements. The problem is that the sheer volume of ads is too overwhelming for manual processing. Ideally, a centralized semi-automated tool can be used to assist law enforcement agencies with this task. Here, we present an approach using natural language processing to identify trafficking ads on these websites. We propose a classifier by integrating multiple text feature sets, including the publicly available pre-trained textual language model Bi-directional Encoder Representation from transformers (BERT). In this paper, we demonstrate that a classifier using this composite feature set has significantly better performance compared to any single feature set alone.
Anthology ID:
P19-1114
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1180–1184
Language:
URL:
https://aclanthology.org/P19-1114
DOI:
10.18653/v1/P19-1114
Bibkey:
Cite (ACL):
Saeideh Shahrokh Esfahani, Michael J. Cafarella, Maziyar Baran Pouyan, Gregory DeAngelo, Elena Eneva, and Andy E. Fano. 2019. Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1180–1184, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements (Shahrokh Esfahani et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1114.pdf