Social media as a window into human-wildlife interactions and zoonotic disease risk: an examination of wild pig hunting videos on YouTube

McLean, Hailey E.; Jaebker, Lauren M.; Anderson, Aaron; Teel, Tara L.; Bright, Alan D.; Shwiff, Stephanie A.; Carlisle, Keith M.

doi:10.1080/10871209.2021.1950240

Cited by 14 publications

(15 citation statements)

References 36 publications

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“…Then, they use topological analysis to understand the relationships that one keyword has with another. As for the SMA methods, the following were identified: (i) time analysis, where a timeline of social media is made, identifying the number of publications during a period [6], [31], [38], [39]; (ii) textual analysis, where social media keywords were studied using natural language processing (NLP) [37], [38], [40], [39]; (iii) statistical analysis, where the studies used hypothesis tests, means and percentages [41], [42], [32], [43]; (iv) sentiment analysis, which aims to identify and extract subjective information from social media by combining NLP and machine learning techniques to assign weighted emotional scores [6], [31], [44], [43], [38], [39]; (v) geographic analysis, where media upload coordinates are used to map their geographic locations [38], [39]; and finally (vi) demographic analysis, where age and gender of the social media user are estimated using their first and last name as input [6]. Figure . 4 illustrates this analysis and shows a radar graph of the popularity of the SNA and SMA techniques.…”

Section: Rq2 What Are the Sna/sma Analysis Techniques Used In Agribus...mentioning

confidence: 99%

“…We analyzed the selected papers to extract the researcher's metrics to understand how the methods proposed in primary studies were evaluated. Most articles (∼83%) [6], [31], [32], [38], [39], [40], [41], [42], [43], [45] used statistical methods such as total publications, means, and percentages to describe the results and evaluate the proposal. However, none of them reported an experiment dedicated to evaluating the proposed method.…”

Section: Rq3 What Are the Evaluation Metrics Used?mentioning

confidence: 99%

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Data Analysis in Social Networks for Agribusiness: A Systematic Review

et al. 2023

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The ability of companies to react to changes imposed by the market can be aided by to information acquisition and knowledge generation. Big data technologies, crowdsourcing, and Online Social Networks (OSN) are used for knowledge generation. These technologies have assumed a significant position in agribusiness in recent decades. This work investigates how social network analysis can promote agribusiness to provide a basis for future applications and evaluations. We adopted a hybrid systematic mapping to conduct the investigation. Two hundred twenty-three works that propose solutions for agribusiness were found and categorized. Results showed the most used OSN is Twitter and revealed an increase in the number of studies in this area. The information obtained indicates how social media monitoring can complement traditional decision-making methods in managing and regulating agricultural systems. However, more studies in agribusiness using data analysis tools on social networks are required, considering the importance of social networks on marketing strategies. Based on our results, we discuss some challenges and research directions.

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Section: Rq2 What Are the Sna/sma Analysis Techniques Used In Agribus...mentioning

confidence: 99%

Section: Rq3 What Are the Evaluation Metrics Used?mentioning

confidence: 99%

Data Analysis in Social Networks for Agribusiness: A Systematic Review

et al. 2023

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“…Consequently, OSINT has been used to examine a few types of human–wildlife interactions. Some published surveys have focused on risk of zoonotic disease potential (McLean et al., 2022) and on human attitudes toward vultures (Lambertucci et al., 2021; Margalida & Donázar, 2020) and other taxa (Fidino et al., 2018). Similarly, Boydston et al.…”

Section: Use Of Osint In Conservation Biologymentioning

confidence: 99%

“…Consequently, OSINT has been used to examine a few types of human-wildlife interactions. Some published surveys have focused on risk of zoonotic disease potential (McLean et al, 2022) and on human attitudes toward vultures (Lambertucci et al, 2021;Margalida & Donázar, 2020) and other taxa (Fidino et al, 2018). Similarly, Boydston et al (2018) surveyed videos posted to social media to characterize coyotedog (Canis latrans-Canis lupus familiaris) interactions and to evaluate how those interactions related to animal size.…”

Section: Human-wildlife and Human-environment Interactionsmentioning

confidence: 99%

Open‐source intelligence for conservation biology

Katzner

Thomason

Huhmann³

et al. 2022

Conservation Biology

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Open‐source intelligence (OSINT) evolved in spy agencies but now is rapidly changing many fields of study, from anthropology to zoology. Despite the fact that OSINT occasionally is used in conservation biology, there is little recognition that some tools and frameworks used by conservation professionals are drawn from this well‐established field. In conservation biology, OSINT is sometimes used to evaluate wildlife crime, human–wildlife and human–environment interactions, animal behavior, and questions of distribution and abundance. Recognizing the conceptual foundations of the field would allow expansion of conservation biology, not only in the areas noted above, but also, for example, in study of habitat use, habitat change, and animal behavior. This recognition would also provide frameworks for conceptual advancement, especially in terms of data and privacy management. Failure to recognize the underpinnings of OSINT tools in conservation biology harms the field because it limits how research is framed, thought about, and implemented. Likewise, taking an OSINT perspective to conservation problems, rather than simply thinking in terms of big data, can enrich the field, expand science, and increase knowledge and understanding of biology and biodiversity.

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“…They have shown outstanding efficiency in public mobilization and fundraising for humanitarian initiatives and environmental engagement (Pantti, 2015;Vu et al, 2021). However, the same platforms also present a serious threat in disseminating problematic material (El Bizri et al, 2015;Lenzi et al, 2020;Mclean et al, 2021) that might expand across countries and continents and inflict major environmental damage. Here we document a new trend of "hunting-fishing-experiments" (HFE) for online entertainment.…”

Section: Introductionmentioning

confidence: 99%

Biodiversity Exploitation for Online Entertainment

Khelifa¹,

Mellal²,

Mahdjoub³

et al. 2022

Front. Conserv. Sci.

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Anthropogenic wildlife exploitation threatens biodiversity worldwide. With the emergence of online trading which facilitates the physical movement of wildlife across countries and continents, wildlife conservation is more challenging than ever. One form of wildlife exploitation involves no physical movement of organisms, presenting new challenges. It consists of hunting and fishing “experiments” for monetized online entertainment. Here we analyze >200 online videos of these so-called experiments in the world's largest video platform (YouTube). These videos generated about half a billion views between 2019 and 2020. The number of target species (including threatened animals), videos, and views increased rapidly during this period. The material used in these experiments raises serious ethical questions about animal welfare and the normalization of violence to animals on the Internet. The emergence of this phenomenon highlights the need for online restriction of this type of content to limit the spread of animal cruelty and the damage to global biodiversity. It also sheds light on some conservation gaps in the virtual sphere of the Internet which offers biodiversity-related business models that has the potential to spread globally.

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Social media as a window into human-wildlife interactions and zoonotic disease risk: an examination of wild pig hunting videos on YouTube

Cited by 14 publications

References 36 publications

Data Analysis in Social Networks for Agribusiness: A Systematic Review

Data Analysis in Social Networks for Agribusiness: A Systematic Review

Open‐source intelligence for conservation biology

Biodiversity Exploitation for Online Entertainment

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