Human trafficking (HT) for forced sexual exploitation, often described as modern-day slavery, is a pervasive problem that affects millions of people worldwide. Perpetrators of this crime post advertisements (ads) on behalf of their victims on adult service websites (ASW). These websites typically contain hundreds of thousands of ads including those posted by independent escorts, massage parlor agencies and spammers (fake ads). Detecting suspicious activity in these ads is difficult and developing data-driven methods is challenging due to the hard-to-label, complex and sensitive nature of the data. In this paper, we propose T-Net, which unlike previous solutions, formulates this problem as weakly supervised classification. Since it takes several months to years to investigate a case and obtain a single definitive label, we design domain-specific signals or indicators that provide weak labels. T-Net also looks into connections between ads and models the problem as a graph learning task instead of classifying ads independently. We show that T-Net outperforms all baselines on a real-world dataset of ads by 7% average weighted F1 score. Given that this data contains personally identifiable information, we also present a realistic data generator and provide the first publicly available dataset in this domain which may be leveraged by the wider research community.

Law enforcement and domain experts can detect human trafficking (HT) in online escort websites by analyzing suspicious clusters of connected ads. How can we explain clustering results intuitively and interactively, visualizing potential evidence for experts to analyze? We present TRAFFICVIS, the first interface for cluster-level HT detection and labeling. Developed through months of participatory design with domain experts, TRAFFICVIS provides coordinated views in conjunction with carefully chosen backend algorithms to effectively show spatio-temporal and text patterns to a wide variety of anti-HT stakeholders. We build upon state-of-the-art text clustering algorithms by incorporating shared metadata as a signal of connected and possibly suspicious activity, then visualize the results. Domain experts can use TRAFFICVIS to label clusters as HT, or other, suspicious, but non-HT activity such as spam and scam, quickly creating labeled datasets to enable further HT research. Through domain expert feedback and a usage scenario, we demonstrate TRAFFICVIS's efficacy. The feedback was overwhelmingly positive, with repeated high praises for the usability and explainability of our tool, the latter being vital for indicting possible criminals.

The seminal work of (Charikar, 2002) gives a space efficient sketching algorithm (Simhash) which compresses real-valued vectors to binary vectors while maintaining an estimate of the Cosine similarity between any pairs of original real-valued vectors. In this work, we propose a sketching algorithm – Simsketch – that can be applied on top of the results obtained from Simhash. This further reduces the data dimension while maintaining an estimate of the Cosine similarity between original real-valued vectors. As a consequence, it helps in scaling up the performance of Simhash. We present theoretical bounds of our result and complement it with experimentation on public datasets. Our proposed algorithm is simple, efficient, and therefore can be adopted in practice.

The Spherical $k$-means algorithm proposed by (Dhillon and Modha, 2001) is a popular algorithm for clustering high dimensional datasets. Although their algorithm is simple and easy to implement, a drawback of the same is that it doesn’t provide any provable guarantee on the clustering result. (Endo and Miyamoto, 2015) suggest an adaptive sampling based algorithm (Spherical $k$-means$++$) which gives near optimal results, with high probability. However, their algorithm requires $k$ sequential passes over the entire dataset, which may not be feasible when the dataset and/or the values of $k$ are large. In this work, we propose a Markov chain based sampling algorithm that takes only one pass over the data, and gives close to optimal clustering similar to Spherical $k$-means$++$, i.e., a faster algorithm while maintaining almost the same approximation. We present a theoretical analysis of the algorithm, and complement it with rigorous experiments on real-world datasets. Our proposed algorithm is simple and easy to implement, and can be easily adopted in practice.