Sammanfattning

Illegal wildlife trade is one of the biggest threats to biodiversity conservation, as many species, including iconic species such as rhinoceros and elephant taxa, are targeted for their meat, trophies and other body parts. Over the last years, the scale and nature of illegal wildlife trade has changed dramatically. The Internet is becoming a major market for wildlife products, as it provides cost-effective solutions, vast outreach and anonymity for illegal wildlife traders. A 2014 study by the International Fund for Animal Welfare found 33 000 items for sale on 280 online marketplaces. More recent findings suggest that the illegal market for wildlife has moved to social media and, to a lesser extent, to the dark web.
So far, the use of social media data in conservation science has been limited. There are survey efforts ongoing to determine the quantity, origins and destinations of illegal wildlife trade on social media, which are carried out in a manual, labour-intensive fashion.
Our contribution is a framework to automatically collect data from social media and identify and filter relevant content. We retrieve data from social media platforms’ application programming interfaces (API), then use deep learning methods to identify and filter the contents of social media posts. Deep learning is a machine-learning approach in which input data from training sets is abstracted into a network tree of numerous abstract levels.
We show how such an approach can be used to identify species and wildlife products – e.g. rhino horn and elephant ivory –, their origins, destinations, routes, and involved actors more efficiently from social media data.
Originalspråkengelska
StatusPublicerad - 25 okt 2018
MoE-publikationstypEj behörig
EvenemangMaantieteen päivät - Helsinki
Varaktighet: 24 okt 201826 okt 2018
https://www.helsinki.fi/fi/konferenssit/maantieteen-paivat-2018

Konferens

KonferensMaantieteen päivät
OrtHelsinki
Period24/10/201826/10/2018
Internetadress

Vetenskapsgrenar

  • 1172 Miljövetenskap
  • 518 Medie- och kommunikationsvetenskap

Citera det här

Fink, C. A., & Di Minin, E. (2018). Social Media & The Global Illegal Trade in Wildlife. Abstract från Maantieteen päivät, Helsinki, .
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Social Media & The Global Illegal Trade in Wildlife. / Fink, Christoph Alexander; Di Minin, Enrico.

2018. Abstract från Maantieteen päivät, Helsinki, .

Forskningsoutput: KonferensbidragSammanfattning

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T1 - Social Media & The Global Illegal Trade in Wildlife

AU - Fink, Christoph Alexander

AU - Di Minin, Enrico

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N2 - Illegal wildlife trade is one of the biggest threats to biodiversity conservation, as many species, including iconic species such as rhinoceros and elephant taxa, are targeted for their meat, trophies and other body parts. Over the last years, the scale and nature of illegal wildlife trade has changed dramatically. The Internet is becoming a major market for wildlife products, as it provides cost-effective solutions, vast outreach and anonymity for illegal wildlife traders. A 2014 study by the International Fund for Animal Welfare found 33 000 items for sale on 280 online marketplaces. More recent findings suggest that the illegal market for wildlife has moved to social media and, to a lesser extent, to the dark web.So far, the use of social media data in conservation science has been limited. There are survey efforts ongoing to determine the quantity, origins and destinations of illegal wildlife trade on social media, which are carried out in a manual, labour-intensive fashion.Our contribution is a framework to automatically collect data from social media and identify and filter relevant content. We retrieve data from social media platforms’ application programming interfaces (API), then use deep learning methods to identify and filter the contents of social media posts. Deep learning is a machine-learning approach in which input data from training sets is abstracted into a network tree of numerous abstract levels. We show how such an approach can be used to identify species and wildlife products – e.g. rhino horn and elephant ivory –, their origins, destinations, routes, and involved actors more efficiently from social media data.

AB - Illegal wildlife trade is one of the biggest threats to biodiversity conservation, as many species, including iconic species such as rhinoceros and elephant taxa, are targeted for their meat, trophies and other body parts. Over the last years, the scale and nature of illegal wildlife trade has changed dramatically. The Internet is becoming a major market for wildlife products, as it provides cost-effective solutions, vast outreach and anonymity for illegal wildlife traders. A 2014 study by the International Fund for Animal Welfare found 33 000 items for sale on 280 online marketplaces. More recent findings suggest that the illegal market for wildlife has moved to social media and, to a lesser extent, to the dark web.So far, the use of social media data in conservation science has been limited. There are survey efforts ongoing to determine the quantity, origins and destinations of illegal wildlife trade on social media, which are carried out in a manual, labour-intensive fashion.Our contribution is a framework to automatically collect data from social media and identify and filter relevant content. We retrieve data from social media platforms’ application programming interfaces (API), then use deep learning methods to identify and filter the contents of social media posts. Deep learning is a machine-learning approach in which input data from training sets is abstracted into a network tree of numerous abstract levels. We show how such an approach can be used to identify species and wildlife products – e.g. rhino horn and elephant ivory –, their origins, destinations, routes, and involved actors more efficiently from social media data.

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Fink CA, Di Minin E. Social Media & The Global Illegal Trade in Wildlife. 2018. Abstract från Maantieteen päivät, Helsinki, .