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

Catalina Vajiac, Duen Horng Chau, Andreas Olligschlaeger

IEEE Transactions on Visualization and Computer Graphics

|October 6, 2022

Summary

This summary is machine-generated.

Law enforcement can now detect human trafficking (HT) online using TRAFFICVIS, an interactive tool that visualizes ad clusters. This system helps experts identify and label suspicious activity, aiding further research and prosecution.

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Area of Science:

Computer Science
Criminology
Information Visualization

Background:

Online escort websites are used for human trafficking (HT).
Detecting HT requires analyzing clusters of connected advertisements.
Existing methods lack intuitive and interactive visualization for expert analysis.

Purpose of the Study:

To introduce TRAFFICVIS, the first interface for cluster-level HT detection and labeling.
To provide an interactive and explainable tool for law enforcement and domain experts.
To facilitate the creation of labeled datasets for further HT research.

Main Methods:

Developed TRAFFICVIS through participatory design with domain experts.
Utilized state-of-the-art text clustering algorithms enhanced with shared metadata.
Implemented coordinated views to visualize spatio-temporal and text patterns.

Main Results:

TRAFFICVIS effectively visualizes patterns for anti-HT stakeholders.
Domain experts can intuitively label clusters as HT or other suspicious activities.
Demonstrated efficacy through expert feedback and a usage scenario.

Conclusions:

TRAFFICVIS is a highly usable and explainable tool for HT detection.
The tool's explainability is crucial for building cases against traffickers.
TRAFFICVIS supports efficient labeling of datasets, advancing HT research and combating online exploitation.