Matsick, Bethany

Liberty University Digital Commons

Abstract SampleTitle – Preventing Human Trafficking with Machine Learning	Program of Study – EngineeringPresentation Type – Choose one of the following: Three Minute ThesisSubtype – Choose one of the following for poster or oral presentation types: AppliedMentor(s) and Mentor Email - Dr. Hector Medina (hmedina@liberty.edu)Student name(s) and email(s) – Bethany Matsick (blmatsick@liberty.edu)Abstract: Human trafficking is a global epidemic that has seen drastic growth in the past several years. Sex trafficking, specifically, has risen significantly due to the widespread availability of escort websites. Such websites provide traffickers an easy method of selling the services of their victims and contribute substantially to total trafficking revenue. With millions of advertisements posted online, it is impossible for law enforcement agencies to manually sift through each advertisement for information. Therefore, it is vital to have an automated method by which to identity trafficking ads. In 2017, Tong et al. presented a novel dataset called Trafficking-10k that contains over 12,000 advertisements, each with a corresponding likelihood of being a sex trafficking ad. Each of the 12,000 advertisements is hand-labeled by an expert analyst to be a likely case of trafficking or not. Using this dataset, we develop a graph neural network to identify escort ads that are likely to be linked to sex trafficking. Unfortunately, this is not as simple as it sounds. Human trafficking advertisements contain hundreds of different emojis and non-English characters that an algorithm must learn and analyze. Furthermore, the average trafficking advertisement is less than 150 words, making it difficult to glean helpful information. Various natural language processing techniques must be implemented before converting the ads into graph form. After advertisements are represented as graphs, they can be fed into the graph neural network and associations and predictions can be made. If successful, the method will save human analysts countless hours of sifting through ads and will be able to accurately pinpoint human trafficking advertisements in real time.

Preventing Human Trafficking with Machine Learning

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