LIPIcs.GISCIENCE.2018.34.pdf
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Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names (Short Paper)
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10th International Conference on Geographic Information Science (GIScience 2018)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Geographic Information Science (GIScience) - License:
Creative Commons Attribution 3.0 Unported license
- Publication date: 2018-08-02
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Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert. Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 34:1-34:6, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.34
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.34
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.
Subject Classification
ACM Subject Classification
- Computing methodologies → Machine learning
- Information systems
Keywords
- Social media
- Text mining
- Volunteered Geographic Information
- Ecology
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References
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