2018/05/27 by Yang Yang, Yang, Yang, Xia Hu +9
Computer Science · Social Sciences · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Sex work and related issues #Social and Information Networks (cs.SI) #Spam and Phishing Detection #Text and Document Classification Technologies
paper · pdf · doi:10.48550/arxiv.1805.10617
openalex publication_date 2018/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Human trafficking is a serious social problem, and it is challenging mainly because of its difficulty in collecting and organizing related information. With the increasing popularity of social media platforms, it provides us a novel channel to tackle the problem of human trafficking through detecting and analyzing a large amount of human trafficking related information. Existing supervised learning methods cannot be directly applied to this problem due to the distinct characteristics of the social media data. First, the short, noisy, and unstructured textual information makes traditional learning algorithms less effective in detecting human trafficking related tweets. Second, complex social interactions lead to a high-dimensional feature space and thus present great computational challenges. In the meanwhile, social sciences theories such as homophily have been well established and achieved success in various social media mining applications. Motivated by the sociological findings, in this paper, we propose to investigate whether the Network Structure Information (NSI) could be potentially helpful for the human trafficking problem. In particular, a novel mathematical optimization framework is proposed to integrate the network structure into content modeling. Experimental results on a real-world dataset demonstrate the effectiveness of our proposed framework in detecting human trafficking related information.