Sammanfattning

Despite the increasing wealth of user-generated content posted online, the use of data mined from social media platforms is still limited in conservation science and practice 1. Many social media platforms provide an application programming interface that allows access to user-generated text, images and videos, as well as to accompanying metadata, such as where and when the content was uploaded, and connections between users. Here, we first demonstrate how data mined from social media platforms can be used to inform national park management and planning. Specifically, we show the usability of different social media platforms (Instagram, Twitter and Flickr) in estimating the visitation rates in national parks 2. We also show that social media data can be used to understand tourists’ preferences for biodiversity experiences and assess what kind of activities tourists conduct when visiting national parks. In both cases, social media data performed as well as data generated from traditional visitor surveys and counters. Second, we show how social media data offer a new means of investigating the illegal wildlife trade that is booming online. Specifically, we show how machine-learning algorithms offer new possibilities to automatically identify content pertaining to the illegal wildlife trade from high-volume data mined from social media platforms 3. We also investigate whether poachers can use information posted by national parks’ visitors to locate and kill commercially valuable species.

References
1 Di Minin E, Tenkanen H, Toivonen T. 2015. Prospects and challenges for social media data in conservation science. Frontiers in Environmental Science 3: 63.
2 Tenkanen, H., Di Minin, E., Heikinheimo, V., Hausmann, A., Toivonen, T. Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas. 2017. Scientific Reports 7: 17615.
3 Di Minin, E., Fink, C., Tenkanen, H., Hiippala, T. 2018. Machine learning for tracking illegal wildlife trade on social media. Nature Ecology and Evolution, DOI: 10.1038/s41559-018-0466-x.

Originalspråkengelska
DOI
StatusPublicerad - 14 jun 2018
MoE-publikationstypEj behörig
EvenemangEuropean Congress of Conservation Biology - Jyväskylä, Finland
Varaktighet: 12 jun 201815 jun 2018
Konferensnummer: 5

Konferens

KonferensEuropean Congress of Conservation Biology
Förkortad titelECCB2018
LandFinland
OrtJyväskylä
Period12/06/201815/06/2018

Citera det här

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title = "Social media data for conservation science and practice",
abstract = "Despite the increasing wealth of user-generated content posted online, the use of data mined from social media platforms is still limited in conservation science and practice 1. Many social media platforms provide an application programming interface that allows access to user-generated text, images and videos, as well as to accompanying metadata, such as where and when the content was uploaded, and connections between users. Here, we first demonstrate how data mined from social media platforms can be used to inform national park management and planning. Specifically, we show the usability of different social media platforms (Instagram, Twitter and Flickr) in estimating the visitation rates in national parks 2. We also show that social media data can be used to understand tourists’ preferences for biodiversity experiences and assess what kind of activities tourists conduct when visiting national parks. In both cases, social media data performed as well as data generated from traditional visitor surveys and counters. Second, we show how social media data offer a new means of investigating the illegal wildlife trade that is booming online. Specifically, we show how machine-learning algorithms offer new possibilities to automatically identify content pertaining to the illegal wildlife trade from high-volume data mined from social media platforms 3. We also investigate whether poachers can use information posted by national parks’ visitors to locate and kill commercially valuable species. References1 Di Minin E, Tenkanen H, Toivonen T. 2015. Prospects and challenges for social media data in conservation science. Frontiers in Environmental Science 3: 63.2 Tenkanen, H., Di Minin, E., Heikinheimo, V., Hausmann, A., Toivonen, T. Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas. 2017. Scientific Reports 7: 17615.3 Di Minin, E., Fink, C., Tenkanen, H., Hiippala, T. 2018. Machine learning for tracking illegal wildlife trade on social media. Nature Ecology and Evolution, DOI: 10.1038/s41559-018-0466-x.",
author = "{Di Minin}, Enrico and Fink, {Christoph Alexander} and Anna Hausmann and Heikinheimo, {Vuokko Vilhelmiina} and Tuomo Hiippala and Tenkanen, {Henrikki Toivo Olavi} and Toivonen, {Tuuli Kaarina}",
year = "2018",
month = "6",
day = "14",
doi = "10.17011/conference/eccb2018/107693",
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note = "European Congress of Conservation Biology, ECCB2018 ; Conference date: 12-06-2018 Through 15-06-2018",

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Social media data for conservation science and practice. / Di Minin, Enrico; Fink, Christoph Alexander; Hausmann, Anna; Heikinheimo, Vuokko Vilhelmiina; Hiippala, Tuomo; Tenkanen, Henrikki Toivo Olavi; Toivonen, Tuuli Kaarina.

2018. Abstract från ECCB 2018 - European Congress of Conservation Biology , Jyväskylä, Finland.

Forskningsoutput: KonferensbidragSammanfattning

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T1 - Social media data for conservation science and practice

AU - Di Minin, Enrico

AU - Fink, Christoph Alexander

AU - Hausmann, Anna

AU - Heikinheimo, Vuokko Vilhelmiina

AU - Hiippala, Tuomo

AU - Tenkanen, Henrikki Toivo Olavi

AU - Toivonen, Tuuli Kaarina

PY - 2018/6/14

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N2 - Despite the increasing wealth of user-generated content posted online, the use of data mined from social media platforms is still limited in conservation science and practice 1. Many social media platforms provide an application programming interface that allows access to user-generated text, images and videos, as well as to accompanying metadata, such as where and when the content was uploaded, and connections between users. Here, we first demonstrate how data mined from social media platforms can be used to inform national park management and planning. Specifically, we show the usability of different social media platforms (Instagram, Twitter and Flickr) in estimating the visitation rates in national parks 2. We also show that social media data can be used to understand tourists’ preferences for biodiversity experiences and assess what kind of activities tourists conduct when visiting national parks. In both cases, social media data performed as well as data generated from traditional visitor surveys and counters. Second, we show how social media data offer a new means of investigating the illegal wildlife trade that is booming online. Specifically, we show how machine-learning algorithms offer new possibilities to automatically identify content pertaining to the illegal wildlife trade from high-volume data mined from social media platforms 3. We also investigate whether poachers can use information posted by national parks’ visitors to locate and kill commercially valuable species. References1 Di Minin E, Tenkanen H, Toivonen T. 2015. Prospects and challenges for social media data in conservation science. Frontiers in Environmental Science 3: 63.2 Tenkanen, H., Di Minin, E., Heikinheimo, V., Hausmann, A., Toivonen, T. Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas. 2017. Scientific Reports 7: 17615.3 Di Minin, E., Fink, C., Tenkanen, H., Hiippala, T. 2018. Machine learning for tracking illegal wildlife trade on social media. Nature Ecology and Evolution, DOI: 10.1038/s41559-018-0466-x.

AB - Despite the increasing wealth of user-generated content posted online, the use of data mined from social media platforms is still limited in conservation science and practice 1. Many social media platforms provide an application programming interface that allows access to user-generated text, images and videos, as well as to accompanying metadata, such as where and when the content was uploaded, and connections between users. Here, we first demonstrate how data mined from social media platforms can be used to inform national park management and planning. Specifically, we show the usability of different social media platforms (Instagram, Twitter and Flickr) in estimating the visitation rates in national parks 2. We also show that social media data can be used to understand tourists’ preferences for biodiversity experiences and assess what kind of activities tourists conduct when visiting national parks. In both cases, social media data performed as well as data generated from traditional visitor surveys and counters. Second, we show how social media data offer a new means of investigating the illegal wildlife trade that is booming online. Specifically, we show how machine-learning algorithms offer new possibilities to automatically identify content pertaining to the illegal wildlife trade from high-volume data mined from social media platforms 3. We also investigate whether poachers can use information posted by national parks’ visitors to locate and kill commercially valuable species. References1 Di Minin E, Tenkanen H, Toivonen T. 2015. Prospects and challenges for social media data in conservation science. Frontiers in Environmental Science 3: 63.2 Tenkanen, H., Di Minin, E., Heikinheimo, V., Hausmann, A., Toivonen, T. Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas. 2017. Scientific Reports 7: 17615.3 Di Minin, E., Fink, C., Tenkanen, H., Hiippala, T. 2018. Machine learning for tracking illegal wildlife trade on social media. Nature Ecology and Evolution, DOI: 10.1038/s41559-018-0466-x.

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