Extracting and Visualizing Wildlife Trafficking Events from Wildlife Trafficking Reports

TL;DR

This paper presents an NLP-based method to automatically extract wildlife trafficking events from reports, enabling trend analysis and visualization to assist experts in combating illegal wildlife trade.

Contribution

It introduces an enhanced NLP pipeline with custom entity recognition to improve event extraction accuracy from wildlife trafficking reports.

Findings

Successfully identified 15 fully correct events and 36 partially correct events

Outperformed baseline with no fully correct events

Enabled interactive visualizations of trafficking trends

Abstract

Experts combating wildlife trafficking manually sift through articles about seizures and arrests, which is time consuming and make identifying trends difficult. We apply natural language processing techniques to automatically extract data from reports published by the Eco Activists for Governance and Law Enforcement (EAGLE). We expanded Python spaCy's pre-trained pipeline and added a custom named entity ruler, which identified 15 fully correct and 36 partially correct events in 15 reports against an existing baseline, which did not identify any fully correct events. The extracted wildlife trafficking events were inserted to a database. Then, we created visualizations to display trends over time and across regions to support domain experts. These are accessible on our website, Wildlife Trafficking in Africa (https://wildlifemqp.github.io/Visualizations/).