TRAFFICVIS: Visualizing Organized Activity and Spatio-Temporal Patterns for Detecting and Labeling Human Trafficking

Vajiac, Catalina; Chau, Duen Horng; Olligschlaeger, Andreas; Mackenzie, Rebecca B.; Nair, Pratheeksha; Lee, Meng-Chieh; Li, Yifei; Park, Namyong; Rabbany, Reihaneh; Faloutsos, Christos

doi:10.1109/tvcg.2022.3209403

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…Nonetheless, our research demonstrates that only 37% of the ads in our dataset contained phone numbers. While clustering approaches (Lee et al, 2021;Vajiac et al, 2023a;Vajiac et al, 2023b) can assist in connecting nearduplicate ads, they fail to establish connections in paraphrased and distinct ads. Therefore, we focus our research on leveraging authorship techniques to analyze unique writing styles within escort ads and establish connections with individual vendors.…”

Section: Related Researchmentioning

confidence: 99%

IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements

Saxena,

Ashpole,

van Dijck

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Human trafficking (HT) is a pervasive global issue affecting vulnerable individuals, violating their fundamental human rights. Investigations reveal that many HT cases are associated with online advertisements (ads), particularly in escort markets. Consequently, identifying and connecting HT vendors has become increasingly challenging for Law Enforcement Agencies (LEAs). To address this issue, we introduce IDTraffickers, an extensive dataset consisting of 87,595 text ads and 5,244 vendor labels to enable the verification and identification of potential HT vendors on online escort markets. To establish a benchmark for authorship identification, we train a DeCLUTR-small model, achieving a macro-F1 score of 0.8656 in a closed-set classification environment. Next, we leverage the style representations extracted from the trained classifier to conduct authorship verification, resulting in a mean r-precision score of 0.8852 in an open-set ranking environment. Finally, to encourage further research and ensure responsible data sharing, we plan to release IDTraffickers for the authorship attribution task to researchers under specific conditions, considering the sensitive nature of the data. We believe that the availability of our dataset and benchmarks will empower future researchers to utilize our findings, thereby facilitating the effective linkage of escort ads and the development of more robust approaches for identifying HT indicators 1 .

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Section: Related Researchmentioning

confidence: 99%

IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements

Saxena,

Ashpole,

van Dijck

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…To address this issue, text clustering and ranking based approaches have been proposed (Kulshrestha 2021;Lee et al 2021) which not only make it easier to analyze the ads but also facilitate their interactive visualizations (Nair et al 2022;Vajiac et al 2022). These approaches are based on one critical insight on HT detection that traffickers tend to almost entirely control the ad content for their victims.…”

Section: Introductionmentioning

confidence: 99%

T-NET: Weakly Supervised Graph Learning for Combatting Human Trafficking

Nair,

Liu,

Vajiac

et al. 2024

AAAI

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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.

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TRAFFICVIS: Visualizing Organized Activity and Spatio-Temporal Patterns for Detecting and Labeling Human Trafficking

Cited by 2 publications

References 31 publications

IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements

IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements

T-NET: Weakly Supervised Graph Learning for Combatting Human Trafficking

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