A framework for investigating illegal wildlife trade on social media with machine learning

Conserv Biol. 2019 Feb;33(1):210-213. doi: 10.1111/cobi.13104. Epub 2018 Nov 14.

Authors

Enrico Di Minin^{1

2

3}, Christoph Fink^{1

2}, Tuomo Hiippala^{1

2

4}, Henrikki Tenkanen^{1

2}

Affiliations

¹ Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki, Helsinki, FI 00014, Finland.
² Helsinki Institute of Sustainability Science, University of Helsinki, Helsinki, FI 00014, Finland.
³ School of Life Sciences, University of KwaZulu-Natal, KwaZulu-Natal, Durban 4000, South Africa.
⁴ Department of Languages, University of Helsinki, Helsinki, FI 00014, Finland.

Abstract

Article impact statement: Machine learning can be used to automatically monitor and assess illegal wildlife trade on social media platforms.

© 2018 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Animals, Wild
Commerce
Conservation of Natural Resources
Machine Learning
Social Media*