Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

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

[thumbnail of GIScience18_camera_ready_.pdf]
Preview
PDF - Accepted Post-Print Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview

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: 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: 23 Oct 2022 14:04
URI: https://orca.cardiff.ac.uk/id/eprint/112684

Citation Data

Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics