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Mapping wildlife species distribution with social media: Augmenting text classification with species names
Authors
Shelan S. Jeawak
Christopher B. Jones
Steven Schockaert
Publication date
1 January 2018
Publisher
Schloss Dagstuhl - Leibniz-Zentrum für Informatik
Doi
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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
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Last time updated on 06/08/2018
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