Internet scientific name frequency as an indicator of cultural salience of biodiversity

Science of The Total Environment

Roberts

et al. 2019

Self Cite

Attention directed at different species by society and science is particularly relevant within the field of conservation, as societal preferences will strongly impact support for conservation initiatives and their success. Here, we assess the association between societal and research interests in four charismatic and threatened species groups, derived from a range of different online sources and social media platforms as well as scientific publications. We found a high level of concordance between scientific and societal taxonomic attention, which was consistent among assessed species groups and media sources. Results indicate that research is apparently not as disconnected from the interests of society as it is often reproached, and that societal support for current research objectives should be adequate. While the high degree of similarity between scientific and societal interest is both striking and satisfying, the dissimilarities are also interesting, as new scientific findings may constitute a constant source of novel interest for the society. In that respect, additional efforts will be necessary to draw scientific and societal focus towards less charismatic species that are in urgent need of research and conservation attention.

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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On the overlap between scientific and societal taxonomic attentions — Insights for conservation

Jarić

Science of The Total Environment

Roberts

et al. 2019

Self Cite

“…In order to account for the problems associated with vernacular species names, such as synonyms, homonyms and multiple meanings, we used the approach proposed by Jarić et al (2016) and Correia et al (2017). Species lists, comprising diurnal birds of prey (orders Accipitriformes, Falconiformes and Cathartiformes), Carnivora, Primates and marine mammals (cetaceans and pinnipeds), were obtained from the IUCN Red List database (IUCN 2017).…”

Section: Methodsmentioning

confidence: 99%

On the overlap between scientific and societal taxonomic attention - insights for conservation

Jarić

Roberts

et al. 2018

Preprint

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“…In general, homonyms present a unique data-mining challenge to separate signal from noise because context cannot be derived from the words alone (Rahm & Bernstein 2001;Tzanis 2014). Disentangling homonyms and similar problems have been raised in other attempts to analyze and mine large text corpora in various fields such as disambiguation in medical literature (Krauthammer & Nenadic 2004;Schuemie et al 2005), patent retrieval (Raffo & Lhuillery 2009), classification of deep-web sources (Xu et al 2007), as well as conservation (Ladle et al 2016;Roll et al 2016;Correia et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to disentangle homonyms in large text corpora

Roll

Berger‐Tal

2018

Conservation Biology

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Systematic reviews are an increasingly popular decision-making tool that provides an unbiased summary of evidence to support conservation action. These reviews bridge the gap between researchers and managers by presenting a comprehensive overview of all studies relating to a particular topic and identify specifically where and under which conditions an effect is present. However, several technical challenges can severely hinder the feasibility and applicability of systematic reviews, for example, homonyms (terms that share spelling but differ in meaning). Homonyms add noise to search results and cannot be easily identified or removed. We developed a semiautomated approach that can aid in the classification of homonyms among narratives. We used a combination of automated content analysis and artificial neural networks to quickly and accurately sift through large corpora of academic texts and classify them to distinct topics. As an example, we explored the use of the word reintroduction in academic texts. Reintroduction is used within the conservation context to indicate the release of organisms to their former native habitat; however, a Web of Science search for this word returned thousands of publications in which the term has other meanings and contexts. Using our method, we automatically classified a sample of 3000 of these publications with over 99% accuracy, relative to a manual classification. Our approach can be used easily with other homonyms and can greatly facilitate systematic reviews or similar work in which homonyms hinder the harnessing of large text corpora. Beyond homonyms we see great promise in combining automated content analysis and machine-learning methods to handle and screen big data for relevant information in conservation science.