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Machine learning for tracking illegal wildlife trade on social media

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

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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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  3. Henrikki Tenkanen
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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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