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License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GISCIENCE.2018.34
URN: urn:nbn:de:0030-drops-93626
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9362/
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Jeawak, Shelan S. ; Jones, Christopher B. ; Schockaert, Steven

Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names (Short Paper)

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LIPIcs-GISCIENCE-2018-34.pdf (0.4 MB)

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.

BibTeX - Entry

@InProceedings{jeawak_et_al:LIPIcs:2018:9362,
  author =	{Shelan S. Jeawak and Christopher B. Jones and Steven Schockaert},
  title =	{{Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names (Short Paper)}},
  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 =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9362},
  URN =		{urn:nbn:de:0030-drops-93626},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.34},
  annote =	{Keywords: Social media, Text mining, Volunteered Geographic Information, Ecology}
}

Keywords: Social media, Text mining, Volunteered Geographic Information, Ecology
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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