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: | Professional Services > Advanced Research Computing @ Cardiff (ARCCA) Schools > 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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