VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets

Challenge:	Anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets.
Approach:	They propose an NLP-based approach that examines writing patterns to verify, identify, and link unique vendor accounts across text advertisements on seven public Darknet markets.
Outcome:	The proposed approach can help law enforcement agencies make more informed decisions by verifying and identifying migrating vendors and their potential aliases on existing and Low-Resource (LR) emerging Darknet markets.

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Challenge:	a significant number of human trafficking cases are associated with online advertisements . identification of HT vendors is challenging for law enforcement agencies .
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Outcome:	IDTraffickers is a dataset that enables verification and identification of HT vendors . the model achieves a macro-F1 score in a closed-set classification environment .

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Challenge:	The first workshop on crowdsourcing for NLP is open to all .
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Outcome:	The proposed approach outperforms existing methods in four darknet forums with a lift of up to 2.5X on the mean retrieval rank and 2X on recall@10.

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Challenge:	EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China .
Approach:	EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . call for papers for this second workshop met with a strong response .
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Challenge:	Human trafficking is a worldwide crisis.
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Challenge:	Methods for Anomaly Detection in text have shown strong empirical results on ad-hoc anomaly setups that are usually made by downsampling some classes of a labeled dataset.
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