  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.
