Abstract
Wildlife trafficking remains a critical global issue, significantly impacting
biodiversity, ecological stability, and public health. Despite efforts to
combat this illicit trade, the rise of e-commerce platforms has made it easier
to sell wildlife products, putting new pressure on wild populations of
endangered and threatened species. The use of these platforms also opens a new
opportunity: as criminals sell wildlife products online, they leave digital
traces of their activity that can provide insights into trafficking activities
as well as how they can be disrupted. The challenge lies in finding these
traces. Online marketplaces publish ads for a plethora of products, and
identifying ads for wildlife-related products is like finding a needle in a
haystack. Learning classifiers can automate ad identification, but creating
them requires costly, time-consuming data labeling that hinders support for
diverse ads and research questions. This paper addresses a critical challenge
in the data science pipeline for wildlife trafficking analytics: generating
quality labeled data for classifiers that select relevant data. While large
language models (LLMs) can directly label advertisements, doing so at scale is
prohibitively expensive. We propose a cost-effective strategy that leverages
LLMs to generate pseudo labels for a small sample of the data and uses these
labels to create specialized classification models. Our novel method
automatically gathers diverse and representative samples to be labeled while
minimizing the labeling costs. Our experimental evaluation shows that our
classifiers achieve up to 95% F1 score, outperforming LLMs at a lower cost. We
present real use cases that demonstrate the effectiveness of our approach in
enabling analyses of different aspects of wildlife trafficking.