  Sex trafficking is a global epidemic. Escort websites are a primary vehicle
for selling the services of such trafficking victims and thus a major driver of
trafficker revenue. Many law enforcement agencies do not have the resources to
manually identify leads from the millions of escort ads posted across dozens of
public websites. We propose an ordinal regression neural network to identify
escort ads that are likely linked to sex trafficking. Our model uses a modified
cost function to mitigate inconsistencies in predictions often associated with
nonparametric ordinal regression and leverages recent advancements in deep
learning to improve prediction accuracy. The proposed method significantly
improves on the previous state-of-the-art on Trafficking-10K, an
expert-annotated dataset of escort ads. Additionally, because traffickers use
acronyms, deliberate typographical errors, and emojis to replace explicit
keywords, we demonstrate how to expand the lexicon of trafficking flags through
word embeddings and t-SNE.
