Project

Investigating illegal wildlife trade on social media

The illegal wildlife trade is arguably considered the largest illegitimate business after narcotics and is threatening the persistence of thousands of species globally. Currently, there is a global spotlight on combating illegal wildlife trade. A paucity of data on the scale and extent of the problem has thus far limited progress toward assessing the real impact illegal wildlife trade is having on biodiversity. In this project, we will address this limitation by using new data made available by project collaborators and data mined from social media platforms. Specifically, we will use this data to develop innovative analyses to expose the supply chain of the illegal wildlife trade in order to inform global conservation policy.

Internet is quickly becoming a major market for wildlife products, as it provides cost-effective solutions, vast outreach and anonymity for illegal wildlife traders. While law enforcement actions have partially been successful in controlling illegal wildlife trade on major e-commerce platforms, the trade appears to have moved to alternative platforms, such as the ‘dark web’ and social media. Recent evidence suggests that illegal wildlife trade over the ‘dark web’ is occurring only in small quantities. With estimated 2.5 billion users, the ease of access has turned social media into a key venue for illegal wildlife trade. While trade on social media currently poses a serious threat to biodiversity conservation (e.g. by increasing demand for wildlife products via networks of users), the wealth of data, which can be mined from these platforms, provides the opportunity to investigate the illegal wildlife trade at an unprecedented spatio-temporal scale. This information, in fact, contains metadata for geographical location and a time stamp indicating when the content was uploaded to the service, as well as other important information, including the networks of users.

Currently, the lack of tools for efficient monitoring of high volume data limits the capability of studying the illegal wildlife trade on social media platforms. In fact, identification of species and/or their parts traded on social media is manual and time-consuming, potentially leading to outdated and ineffective solutions to the problem. Automating the information extraction procedure is therefore crucial. Using advanced computational techniques and tools to monitor and assess the extent of illegal wildlife trade across various social media platforms is a frontier topic in conservation science. Despite their potential, approaches combining new techniques and data sources are still rarely used to help address the biodiversity crisis. Together with my collaborators, we are among the first ones to successfully use social media data to address important research questions in conservation science. We now plan to develop advanced machine learning tools that can be used to automatically identify verbal, visual or audio-visual content pertaining illegal wildlife products on social media.

Funders: Academy of Finland and University of Helsinki

 

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