2018
DOI: 10.1111/cobi.13104
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A framework for investigating illegal wildlife trade on social media with machine learning

Abstract: Article impact statement: Machine learning can be used to automatically monitor and assess illegal wildlife trade on social media platforms.

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Cited by 105 publications
(68 citation statements)
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“…It is also generally known that the search on the API often provides many videos that are not relevant to the interest of the studies [e.g. 44,49]. Regarding the analysis exploring factors that affect the social engagement (i.e.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…It is also generally known that the search on the API often provides many videos that are not relevant to the interest of the studies [e.g. 44,49]. Regarding the analysis exploring factors that affect the social engagement (i.e.…”
Section: Discussionmentioning
confidence: 99%
“…For instance, Hausmann et al 2018 [43] demonstrated that social media has the potential to be utilized to remotely and cost-effectively understand tourists' preferences or expectations in wildlife tourism. Di Minin et al 2019 [44] provided a framework to investigate illegal wildlife trade using social media data and machine learning.…”
Section: Introductionmentioning
confidence: 99%
“…But plastic pollution is a singular component of the wider threat of pollution, and, when considered in the fullest context of biodiversity loss and long-exceeded planetary boundaries, the focus afforded to this issue is arguably disproportionate to the scale of the threat posed (Stafford & Jones 2019). In contrast, overexploitation is a leading threat to biodiversity (Maxwell et al 2016;Di Minin et al 2019) that occupies a large share of eNGO Twitter advocacy while also being highly socially contagious (Fig. 1b)-a combination that could be responsible for generating positive outcomes for biodiversity conservation.…”
Section: Discussionmentioning
confidence: 99%
“…There are a number of automated approaches for the search and retrieval of social media posts (see e.g. Malik and Tian 2017), which, with the employment of machine learning techniques (Di Minin et al 2018), may further increase the efficiency of similar analyses in the future. Harris et al (2015) suggest that species that are being over-exploited through trade can be identified on the basis of market data and observations of increasing prices alongside decreasing supply -notwithstanding data limitations, the use of social media data to detect increasing activity and/or popularity associated with a specific species or product offers a significant advantage over a market based approach by detecting and highlighting trends in interest well ahead of any more serious effects such as decreasing supply.…”
Section: Social Media Insights As a Wildlife Protection Toolmentioning
confidence: 99%