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

Research output: Contribution to journalArticleScientificpeer-review

Original languageEnglish
JournalConservation Biology
Volume33
Issue number1
Pages (from-to)210-213
Number of pages4
ISSN0888-8892
DOIs
Publication statusPublished - Feb 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 1172 Environmental sciences

Cite this

@article{db6cd5b67c33427083b1be89fa34e06a,
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",
month = "2",
doi = "10.1111/cobi.13104",
language = "English",
volume = "33",
pages = "210--213",
journal = "Conservation Biology",
issn = "0888-8892",
publisher = "John Wiley & Sons, Ltd (10.1111)",
number = "1",

}

A framework for investigating illegal wildlife trade on social media with machine learning. / Di Minin, Enrico; Fink, Christoph; Hiippala, Tuomo; Tenkanen, Henrikki.

In: Conservation Biology, Vol. 33, No. 1, 02.2019, p. 210-213.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - A framework for investigating illegal wildlife trade on social media with machine learning

AU - Di Minin, Enrico

AU - Fink, Christoph

AU - Hiippala, Tuomo

AU - Tenkanen, Henrikki

PY - 2019/2

Y1 - 2019/2

KW - 1172 Environmental sciences

U2 - 10.1111/cobi.13104

DO - 10.1111/cobi.13104

M3 - Article

VL - 33

SP - 210

EP - 213

JO - Conservation Biology

JF - Conservation Biology

SN - 0888-8892

IS - 1

ER -