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.
@InProceedings{jeawak_et_al:LIPIcs.GISCIENCE.2018.34, author = {Jeawak, Shelan S. and Jones, Christopher B. and Schockaert, Steven}, title = {{Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names}}, booktitle = {10th International Conference on Geographic Information Science (GIScience 2018)}, pages = {34:1--34:6}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-083-5}, ISSN = {1868-8969}, year = {2018}, volume = {114}, editor = {Winter, Stephan and Griffin, Amy and Sester, Monika}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.34}, URN = {urn:nbn:de:0030-drops-93626}, doi = {10.4230/LIPIcs.GISCIENCE.2018.34}, annote = {Keywords: Social media, Text mining, Volunteered Geographic Information, Ecology} }
Feedback for Dagstuhl Publishing