Using Wikipedia page views to explore the cultural importance of global reptiles

Abstract

Modern conservation operates at the nexus of biological and social influences. While the importance of social and cultural factors are often mentioned, defining, measuring and comparing them remains a significant challenge. Here, we explore a novel method to quantify cultural interest in all extant reptile species using Wikipedia-a large, open-access online encyclopaedia. We analysed all page views of reptile species viewed during 2014 in all of Wikipedia's language editions. We compared species' page view numbers across languages and in relationship to their spatial distribution, phylogeny, threat status and various other biological attributes. We found that while the top three species with respect to page views are shared across major language editions, beyond these, page view ranks of species tend to be specific to particular language editions. Interest within a language is mostly focused on reptiles found in the regions where the language is spoken. Overall, interest is greater for reptiles that are venomous, endangered, widely distributed, larger sized and that have been described earlier. However, within individual families not all the above factors predict page views. Most families contain at least one species in the top 5% of page views, but 29 families (with 1450 species) have no 'high interest species' in them. Overall, our analyses elucidate novel patterns of human interests in nature over large geographical, cultural and taxonomic spectra using big-data techniques. Such approaches hold much promise for incorporating social perceptions in future conservation practices.

Assessing the utility of conserving evolutionary history

“…or 'Do people search more frequently online for species with no close relatives and, if so, why?' (Roll et al, 2016). We note that the potential variation in human values across cultures and world views regarding biodiversity may preclude any easy generalities.…”

“…Current studies are limited in number and are rarely designed to test directly whether evolutionary history or phenotypic diversity is linked to human preference directly, or via a sampling effect, whereby more diverse sets tend to include species we prefer. Future studies should consider testing human preference for PD or TR directly, for example asking ‘Do people favour national parks harbouring sets of distantly related species more than parks that are home to sets of closely related species?’ or ‘Do people search more frequently online for species with no close relatives and, if so, why?’ (Roll et al ., ). We note that the potential variation in human values across cultures and world views regarding biodiversity may preclude any easy generalities.…”

Considering connections between Hollywood and biodiversity conservation

“…Recognizing that human actions are increasingly played out in a digital realm, Roll et al. () used page views of the Wikipedia online digital text archive as a metric of global interest in reptiles. Google trends statistics have also been used in relation to conservation (Proulx et al.…”

“…Increasingly, it may be possible to monitor audience responses by looking to their online activity, the advantages and disadvantages of which are discussed by Arts et al (2015). Recognizing that human actions are increasingly played out in a digital realm, Roll et al (2016) used page views of the Wikipedia online digital text archive as a metric of global interest in reptiles. Google trends statistics have also been used in relation to conservation (Proulx et al 2014), for example, to explore factors influencing internet saliency of bird species (Correia et al 2016).…”

Using machine learning to disentangle homonyms in large text corpora

“…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).…”