Pratheeksha Nair

VisPaD: Visualization and Pattern Discovery for Fighting Human Trafficking

Pratheeksha Nair

Yifei Li

Catalina Vajiac

Andreas Olligschlaeger

Meng-Chieh Lee

Namyong Park

Duen Horng Chau

Christos Faloutsos

Reihaneh Rabbany

Chieh Lee

Human trafficking analysts investigate groups of related online escort advertisements (called micro-clusters) to detect suspicious activitie… (voir plus)s and identify various modus operandi. This task is complex as it requires finding patterns and linked meta-data across micro-clusters such as the geographical spread of ads, cluster sizes, etc. Additionally, drawing insights from the data is challenging without visualizing these micro-clusters. To address this, in close-collaboration with domain experts, we built VisPaD, a novel interactive way for characterizing and visualizing micro-clusters and their associated meta-data, all in one place. VisPaD helps discover underlying patterns in the data by projecting micro-clusters in a lower dimensional space. It also allows the user to select micro-clusters involved in suspicious patterns and interactively examine them leading to faster detection and identification of trends in the data. A demo of VisPaD is also released1.

2022-04-25

The Web Conference (published)

doi.org

Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking

2022-01-01

Findings (publié)

doi.org

Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking

Online escort advertisement websites are widely used for advertising victims of human trafficking. Domain experts agree that advertising mul… (voir plus)tiple people in the same ad is a strong indicator of trafficking. Thus, extracting person names from the text of these ads can provide valuable clues for further analysis. However, Named-Entity Recognition (NER) on escort ads is challenging because the text can be noisy, colloquial and often lacking proper grammar and punctuation. Most existing state-of-the-art NER models fail to demonstrate satisfactory performance in this task. In this paper, we propose NEAT (Name Extraction Against Trafficking) for extracting person names. It effectively combines classic rule-based and dictionary extractors with a contextualized language model to capture ambiguous names (e.g penny, hazel) and adapts to adversarial changes in the text by expanding its dictionary. NEAT shows 19% improvement on average in the F1 classification score for name extraction compared to previous state-of-the-art in two domain-specific datasets.

2022-01-01

Findings (published)

doi.org

RAFFIC V IS : Fighting Human Trafﬁcking through Visualization

Catalina Vajiac

Andreas Olligschlaeger

Yifei Li

Pratheeksha Nair

Meng-Chieh Lee

Namyong Park

Reihaneh Rabbany

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… (voir plus)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.

Global Surveillance of COVID-19 by mining news media using a multi-source dynamic embedded topic model

Yue Li

Pratheeksha Nair

Zhi Wen

Imane Chafi

Anya Okhmatovskaia

Guido Powell

Yannan Shen

David Buckeridge

2020-11-10

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (publié)

doi.org