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

Enrico Di Minin; Christoph Fink; Tuomo Hiippala; Henrikki Tenkanen

doi:10.1111/cobi.13104

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

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

Research output: Contribution to journal › Article › Scientific › peer-review

Original language	English
Journal	Conservation Biology
Volume	33
Issue number	1
Pages (from-to)	210-213
Number of pages	4
ISSN	0888-8892
DOIs	https://doi.org/10.1111/cobi.13104
Publication status	Published - Feb 2019
MoE publication type	A1 Journal article-refereed

Fields of Science

1172 Environmental sciences

Access to Document

10.1111/cobi.13104Licence: CC BY-NC-ND

Minin_et_al-2019-Conservation_BiologyFinal published version, 1.43 MBLicence: CC BY-NC-ND

1 Finished

Investigating illegal wildlife trade: innovative approaches to inform global conservation policy
Di Minin, E., Fink, C., Haukka, A. & Hausmann, A.
Valtion perusrahoitus/hankkeet, Helsinki Institute of Sustainability Science (HELSUS)
01/09/2016 → 31/05/2020
Project: University of Helsinki Three-Year Research Project

Cite this

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title = "A framework for investigating illegal wildlife trade on social media with machine learning",

keywords = "1172 Environmental sciences",

author = "{Di Minin}, Enrico and Christoph Fink and Tuomo Hiippala and Henrikki Tenkanen",

year = "2019",

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volume = "33",

pages = "210--213",

journal = "Conservation Biology",

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AU - Di Minin, Enrico

AU - Fink, Christoph

AU - Hiippala, Tuomo

AU - Tenkanen, Henrikki

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Y1 - 2019/2

KW - 1172 Environmental sciences

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JO - Conservation Biology

JF - Conservation Biology

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

Fields of Science

Access to Document

Projects

Investigating illegal wildlife trade: innovative approaches to inform global conservation policy

Cite this