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

Jeawak, Shelan S.; Jones, Christopher B.; Schockaert, Steven

Abstract

© Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert. 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.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 10th International Conference on Geographic Information Science (GIScience 2018)
• Acceptance Date: Jun 1, 2018
• Publication Date: Aug 1, 2018
• Deposit Date: Jun 5, 2020
• Publicly Available Date: Jul 1, 2020
• Journal: Leibniz International Proceedings in Informatics, LIPIcs
• Print ISSN: 1868-8969
• Publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
• Peer Reviewed: Peer Reviewed
• Volume: 114
• ISBN: 9783959770835
• DOI: https://doi.org/10.4230/LIPIcs.GIScience.2018.34
• Keywords: 2012 ACM Subject Classification Computing methodologies → Machine learning; Information systems Keywords and phrases Social media; Text mining; Volunteered Geographic Information; Ecology
• Public URL: https://uwe-repository.worktribe.com/output/5986208