Abstract: 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.

Introduction: Globally, human trafficking is one of the fastest growing crimes and, with annual profits estimated to be in excess of 150 billion USD, it is also among the most lucrative BIBREF0 . Sex trafficking is a form of human trafficking which involves sexual exploitation through coercion. Recent estimates suggest that nearly 4 million adults and 1 million children are being victimized globally on any given day; furthermore, it is estimated that 99 percent of victims are female BIBREF1 . Escort websites are an increasingly popular vehicle for selling the services of trafficking victims. According to a recent survivor survey BIBREF2 , 38% of underage trafficking victims who were enslaved prior to 2004 were advertised online, and that number rose to 75% for those enslaved after 2004. Prior to its shutdown in April 2018, the website Backpage was the most frequently used online advertising platform; other popular escort websites include Craigslist, Redbook, SugarDaddy, and Facebook BIBREF2 . Despite the seizure of Backpage, there were nearly 150,000 new online sex advertisements posted per day in the U.S. alone in late 2018 BIBREF3 ; even with many of these new ads being re-posts of existing ads and traffickers often posting multiple ads for the same victims BIBREF2 , this volume is staggering. Because of their ubiquity and public access, escort websites are a rich resource for anti-trafficking operations. However, many law enforcement agencies do not have the resources to sift through the volume of escort ads to identify those coming from potential traffickers. One scalable and efficient solution is to build a statistical model to predict the likelihood of an ad coming from a trafficker using a dataset annotated by anti-trafficking experts. We propose an ordinal regression neural network tailored for text input. This model comprises three components: (i) a Word2Vec model BIBREF4 that maps each word from the text input to a numeric vector, (ii) a gated-feedback recurrent neural network BIBREF5 that sequentially processes the word vectors, and (iii) an ordinal regression layer BIBREF6 that produces a predicted ordinal label. We use a modified cost function to mitigate inconsistencies in predictions associated with nonparametric ordinal regression. We also leverage several regularization techniques for deep neural networks to further improve model performance, such as residual connection BIBREF7 and batch normalization BIBREF8 . We conduct our experiments on Trafficking-10k BIBREF9 , a dataset of escort ads for which anti-trafficking experts assigned each sample one of seven ordered labels ranging from “1: Very Unlikely (to come from traffickers)” to “7: Very Likely”. Our proposed model significantly outperforms previously published models BIBREF9 on Trafficking-10k as well as a variety of baseline ordinal regression models. In addition, we analyze the emojis used in escort ads with Word2Vec and t-SNE BIBREF10 , and we show that the lexicon of trafficking-related emojis can be subsequently expanded. In Section SECREF2 , we discuss related work on human trafficking detection and ordinal regression. In Section SECREF3 , we present our proposed model and detail its components. In Section SECREF4 , we present the experimental results, including the Trafficking-10K benchmark, a qualitative analysis of the predictions on raw data, and the emoji analysis. In Section SECREF5 , we summarize our findings and discuss future work.

Question: By how much do they outperform previous state-of-the-art models?
Answer: Proposed ORNN has 0.769, 1.238, 0.818, 0.772 compared to 0.778, 1.244, 0.813, 0.781 of best state of the art result on Mean Absolute Error (MAE), macro-averaged Mean Absolute Error (MAEM ), binary classification accuracy (Acc.) and weighted binary classification accuracy (Wt. Acc.)