Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Learning for Detecting Human Trafficking   Human trafficking is one of the most atrocious crimes and among the
challenging problems facing law enforcement which demands attention of global
magnitude. In this study, we leverage textual data from the website "Backpage"-
used for classified advertisement- to discern potential patterns of human
trafficking activities which manifest online and identify advertisements of
high interest to law enforcement. Due to the lack of ground truth, we rely on a
human analyst from law enforcement, for hand-labeling a small portion of the
crawled data. We extend the existing Laplacian SVM and present S3VM-R, by
adding a regularization term to exploit exogenous information embedded in our
feature space in favor of the task at hand. We train the proposed method using
labeled and unlabeled data and evaluate it on a fraction of the unlabeled data,
herein referred to as unseen data, with our expert's further verification.
Results from comparisons between our method and other semi-supervised and
supervised approaches on the labeled data demonstrate that our learner is
effective in identifying advertisements of high interest to law enforcement

12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex