Online auction marketplaces as a global pathway for aquatic invasive species

Olden, Julian D.; Whattam, Ethen; Wood, Spencer A.

doi:10.1007/s10750-020-04407-7

Cited by 44 publications

(36 citation statements)

References 51 publications

(81 reference statements)

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“…In this issue, Forneck et al (2021) demonstrate that aquaculture intensity is a good predictor of catch frequency of a known high-impact invader, Nile tilapia (Oreochromis niloticus, Linnaeus, 1758), in Brazilian streams, which implies that aquaculture ponds are insufficiently isolated to prevent accidental escape and provide a steady source of propagules for invasion. Olden et al (2021) show that ornamental aquarium trade via informal online marketplaces encompassed sales of hundreds of thousands of organisms and introduced a multitude of new, decentralized and difficult to regulate pathways, thereby facilitating global transport of AIS. This study highlights the increasing potential of online markets to become the new frontier of non-native species introductions in the world.…”

Section: Introduction and Establishmentmentioning

confidence: 99%

Aquatic invasive species: introduction to the Special Issue and dynamics of public interest

et al. 2021

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Section: Introduction and Establishmentmentioning

confidence: 99%

Aquatic invasive species: introduction to the Special Issue and dynamics of public interest

et al. 2021

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“…Here, we demonstrate that text classification can be viable tool to identify irrelevant listings. When considering data on the scale of ‘big data’ of tens to hundreds of thousands of online advertisements (e.g., [ 28 ]), text classifiers have the potential to save tens to hundreds of hours of curation effort. We recommend future application of text classifiers and testing other machine learning and natural language processing tools when cleaning messy data collected from the Internet on wildlife trade.…”

Section: Discussionmentioning

confidence: 99%

Text classification to streamline online wildlife trade analyses

et al. 2021

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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.

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“…When considering data on the scale of 'big data' of tens to hundreds of thousands of online advertisements (e.g. Olden et al 2020), text classifiers have the potential to save tens to hundreds of hours of curation effort. We recommend future application of text classifiers and testing other machine learning and natural language processing tools when cleaning messy data collected from the Internet on wildlife trade.…”

Section: Discussionmentioning

confidence: 99%

Text classification to streamline online wildlife trade analyses

Stringham¹,

Moncayo²,

Hill³

et al. 2021

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1.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 of these 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. 2.Here, we test the ability of a suite of text classifiers to extract relevant advertisements from an Australian classifieds website where people can post advertisements of their pet birds (n = 16.5k advertisements). 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.3.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). 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. 4.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.

show abstract

Online auction marketplaces as a global pathway for aquatic invasive species

Cited by 44 publications

References 51 publications

Aquatic invasive species: introduction to the Special Issue and dynamics of public interest

Aquatic invasive species: introduction to the Special Issue and dynamics of public interest

Text classification to streamline online wildlife trade analyses

Text classification to streamline online wildlife trade analyses

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