2026-07-22T16:58:05Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:9362 2024-03-06T10:44:08Z ddc:004 open_access

Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names (Short Paper) Jeawak, Shelan S. Jones, Christopher B. Schockaert, Steven Social media Text mining Volunteered Geographic Information Ecology 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Shelan S. Jeawak and Christopher B. Jones and Steven Schockaert 2018 Is Part Of LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GISCIENCE.2018.34 urn:nbn:de:0030-drops-93626 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.34 eng https://creativecommons.org/licenses/by/3.0/legalcode