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Machine learning for tracking illegal wildlife trade on social media

Nature Ecology & Evolution volume 2, pages 406–407 (2018)Cite this article

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To the Editor — Illegal trade in wildlife is booming on e-commerce platforms1, the ‘dark web’2 and social media1,3. The ease of access and high number of users of social media make it a particularly concerning venue1,3,4. Wildlife dealers use social media to release photos and information about products to attract customers and to market their products to networks of contacts. We currently lack the tools for automated monitoring of high-volume data that are needed to investigate and prevent this illegal trade, but machine-learning algorithms offer a way forward.

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Fig. 1: Filtering Twitter content for rhinoceros images using machine learning.

References

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Acknowledgements

E.D.M. thanks the Academy of Finland 2016–2019, Grant 296524, for support. C.F. thanks the University of Helsinki for support via an Early Career Grant to E.D.M. T.H. was funded by the Finnish Cultural Foundation. H.T. thanks the DENVI doctoral programme at the University of Helsinki for support.

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Authors and Affiliations

  1. Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki, Helsinki, FI-00014, Finland

    Enrico Di Minin, Christoph Fink, Henrikki Tenkanen & Tuomo Hiippala

  2. School of Life Sciences, University of KwaZulu-Natal, Durban, 4000, South Africa

    Enrico Di Minin

  3. Department of Languages, University of Helsinki, Helsinki, FI-00014, Finland

    Tuomo Hiippala

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  1. Enrico Di Minin
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  2. Christoph Fink
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  4. Tuomo Hiippala
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Correspondence to Enrico Di Minin.

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Di Minin, E., Fink, C., Tenkanen, H. et al. Machine learning for tracking illegal wildlife trade on social media. Nat Ecol Evol 2, 406–407 (2018). https://doi.org/10.1038/s41559-018-0466-x

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