Abstract

Illegal wildlife trade (e.g. illegal trade of ivory harvested from poached elephants) is one of the main threats to biodiversity, involving thousands of species. Traditionally, illegal wildlife trade has thrived on physical markets. In this age of global connectivity, however, illegal wildlife trade has moved to online markets, especially social media platforms. Social media and other digital platforms offer good conditions for illegal wildlife trade to thrive, as the platforms are easily accessible and have a high number of users. While this represents a pressing threat to the species targeted in the illegal wildlife trade, scientists can use data mined from digital platforms to investigate illegal wildlife trade at an unprecedented spatio-temporal scale. The use of digital data sources in combination with methods from artificial intelligence can potentially be used to provide new insights, which might help stop illegal wildlife trade. Many social media platforms provide an application programming interface that allows access to user-generated text, images and videos, as well as to accompanying metadata, such as where and when the content was uploaded, and connections between users. This deluge of data can be used to investigate illegal wildlife trade in a cost-efficient manner, but require methods from computer science to be efficiently used in conservation science. In the presentation, I will show (i) how machine learning can be used to automatically identify content pertaining to the illegal wildlife trade from high-volume data mined from social media platforms and (ii) how natural language processing can be used to assess preferences, reactions and sentiment of social media users towards illegal wildlife trade.

Speaker: Enrico Di Minin
Affiliation: Research Fellow in Conservation and Sustainability Science, University of Helsinki