Mapping wildlife species distribution with social media: Augmenting text classification with species names

Jeawak, Shelan, Jones, Christopher ORCID: https://orcid.org/0000-0001-6847-7575 and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 2018. Mapping wildlife species distribution with social media: Augmenting text classification with species names. Presented at: GIScience 2018: 10th International Conference on Geographic Information Science, Melbourne, Australia, 28-31 August 2018. Published in: Winter, Stephan, Griffin, Amy and Sester, Monika eds. 10th International Conference of Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs) , vol.114 Dagstuhl, Germany: Schloss Dagstuhl/Leibniz-Zentrum fuer Informatik, 34:1-34:6. 10.4230/LIPIcs.GISCIENCE.2018.34

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Abstract

Social media has considerable potential as a source of passive citizen science observations of the natural environment, including wildlife monitoring. Here we compare and combine two main strategies for using social media postings to predict species distributions: (i) identifying postings that explicitly mention the target species name and (ii) using a text classifier that exploits all tags to construct a model of the locations where the species occurs. We find that the first strategy has high precision but suffers from low recall, with the second strategy achieving a better overall performance. We furthermore show that even better performance is achieved with a meta classifier that combines data on the presence or absence of species name tags with the predictions from the text classifier.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Computer Science & Informatics
Publisher:	Schloss Dagstuhl/Leibniz-Zentrum fuer Informatik
ISBN:	978-3-95977-083-5
ISSN:	1868-8969
Date of First Compliant Deposit:	18 July 2018
Last Modified:	14 Jun 2024 15:38
URI:	https://orca.cardiff.ac.uk/id/eprint/112684

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