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Yifei Li

Alumni

Publications

TrafficVis: Visualizing Organized Activity and Spatio-Temporal Patterns for Detecting and Labeling Human Trafficking
Catalina Vajiac
Duen Horng Chau
Andreas Olligschlaeger
Rebecca Mackenzie
Meng-Chieh Lee
Namyong Park
Christos Faloutsos
Law enforcement and domain experts can detect human trafficking (HT) in online escort websites by analyzing suspicious clusters of connected… (see more) 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.
VisPaD: Visualization and Pattern Discovery for Fighting Human Trafficking
Catalina Vajiac
Andreas Olligschlaeger
Meng-Chieh Lee
Namyong Park
Duen Horng Chau
Christos Faloutsos
Chieh Lee
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking
RAFFIC V IS : Fighting Human Trafficking through Visualization
Catalina Vajiac
Andreas Olligschlaeger
Meng-Chieh Lee
Namyong Park
Duen Horng Chau
Christos Faloutsos
Law enforcement can detect human trafficking (HT) in online escort websites by analyzing suspicious clusters of connected ads. Given such cl… (see more)usters, how can we interactively visualize potential evidence for law enforcement and domain experts? We present TRAFFICVIS, which, to our knowledge, is the first interface for cluster-level HT detection and labeling. It builds on state-of-the-art HT clustering algorithms by incorporating metadata as a signal of organized and potentially suspicious activity. Also, domain experts can label clusters as HT, spam, and more, efficiently creating labeled datasets to enable further HT research. TRAFFICVIS has been built in close collaboration with domain experts, who estimate that TRAFFICVIS provides a median 36x speedup over manual labeling.