Text classification to streamline online wildlife trade analyses.

Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practition...

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Autores principales:	Oliver C Stringham, Stephanie Moncayo, Katherine G W Hill, Adam Toomes, Lewis Mitchell, Joshua V Ross, Phillip Cassey
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ee7657fc034648dc94b59b6399e461e4
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spelling	oai:doaj.org-article:ee7657fc034648dc94b59b6399e461e42021-12-02T20:09:25ZText classification to streamline online wildlife trade analyses.1932-620310.1371/journal.pone.0254007https://doaj.org/article/ee7657fc034648dc94b59b6399e461e42021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254007https://doaj.org/toc/1932-6203Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practitioners. Given that many wildlife-trade advertisements have an unstructured text format, automated identification of relevant listings has not traditionally been possible, nor attempted. Other scientific disciplines have solved similar problems using machine learning and natural language processing models, such as text classifiers. Here, we test the ability of a suite of text classifiers to extract relevant advertisements from wildlife trade occurring on the Internet. We collected data from an Australian classifieds website where people can post advertisements of their pet birds (n = 16.5k advertisements). We found that text classifiers can predict, with a high degree of accuracy, which listings are relevant (ROC AUC ≥ 0.98, F1 score ≥ 0.77). Furthermore, in an attempt to answer the question 'how much data is required to have an adequately performing model?', we conducted a sensitivity analysis by simulating decreases in sample sizes to measure the subsequent change in model performance. From our sensitivity analysis, we found that text classifiers required a minimum sample size of 33% (c. 5.5k listings) to accurately identify relevant listings (for our dataset), providing a reference point for future applications of this sort. Our results suggest that text classification is a viable tool that can be applied to the online trade of wildlife to reduce time dedicated to data cleaning. However, the success of text classifiers will vary depending on the advertisements and websites, and will therefore be context dependent. Further work to integrate other machine learning tools, such as image classification, may provide better predictive abilities in the context of streamlining data processing for wildlife trade related online data.Oliver C StringhamStephanie MoncayoKatherine G W HillAdam ToomesLewis MitchellJoshua V RossPhillip CasseyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254007 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Oliver C Stringham Stephanie Moncayo Katherine G W Hill Adam Toomes Lewis Mitchell Joshua V Ross Phillip Cassey Text classification to streamline online wildlife trade analyses.
description	Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practitioners. Given that many wildlife-trade advertisements have an unstructured text format, automated identification of relevant listings has not traditionally been possible, nor attempted. Other scientific disciplines have solved similar problems using machine learning and natural language processing models, such as text classifiers. Here, we test the ability of a suite of text classifiers to extract relevant advertisements from wildlife trade occurring on the Internet. We collected data from an Australian classifieds website where people can post advertisements of their pet birds (n = 16.5k advertisements). We found that text classifiers can predict, with a high degree of accuracy, which listings are relevant (ROC AUC ≥ 0.98, F1 score ≥ 0.77). Furthermore, in an attempt to answer the question 'how much data is required to have an adequately performing model?', we conducted a sensitivity analysis by simulating decreases in sample sizes to measure the subsequent change in model performance. From our sensitivity analysis, we found that text classifiers required a minimum sample size of 33% (c. 5.5k listings) to accurately identify relevant listings (for our dataset), providing a reference point for future applications of this sort. Our results suggest that text classification is a viable tool that can be applied to the online trade of wildlife to reduce time dedicated to data cleaning. However, the success of text classifiers will vary depending on the advertisements and websites, and will therefore be context dependent. Further work to integrate other machine learning tools, such as image classification, may provide better predictive abilities in the context of streamlining data processing for wildlife trade related online data.
format	article
author	Oliver C Stringham Stephanie Moncayo Katherine G W Hill Adam Toomes Lewis Mitchell Joshua V Ross Phillip Cassey
author_facet	Oliver C Stringham Stephanie Moncayo Katherine G W Hill Adam Toomes Lewis Mitchell Joshua V Ross Phillip Cassey
author_sort	Oliver C Stringham
title	Text classification to streamline online wildlife trade analyses.
title_short	Text classification to streamline online wildlife trade analyses.
title_full	Text classification to streamline online wildlife trade analyses.
title_fullStr	Text classification to streamline online wildlife trade analyses.
title_full_unstemmed	Text classification to streamline online wildlife trade analyses.
title_sort	text classification to streamline online wildlife trade analyses.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/ee7657fc034648dc94b59b6399e461e4
work_keys_str_mv	AT olivercstringham textclassificationtostreamlineonlinewildlifetradeanalyses AT stephaniemoncayo textclassificationtostreamlineonlinewildlifetradeanalyses AT katherinegwhill textclassificationtostreamlineonlinewildlifetradeanalyses AT adamtoomes textclassificationtostreamlineonlinewildlifetradeanalyses AT lewismitchell textclassificationtostreamlineonlinewildlifetradeanalyses AT joshuavross textclassificationtostreamlineonlinewildlifetradeanalyses AT phillipcassey textclassificationtostreamlineonlinewildlifetradeanalyses
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Text classification to streamline online wildlife trade analyses.

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