Date:
2018/06/14
Time:
11:15
Room:
A2 Wivi
Social media data for conservation science and practice
(Oral)
, , , , , ,
SEE PEER REVIEW
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.