Abstract
Abstract As resources for conservation are limited, gathering and analysing information from digital platforms can help investigate the global biodiversity crisis in a cost‐efficient manner. Development and application of methods for automated content analysis of digital data sources are especially important in the context of investigating human–nature interactions. In this study, we introduce novel application methods to automatically collect and analyse textual data on species of conservation concern from digital platforms. An end‐to‐end pipeline is constructed that begins from searching and downloading news articles about species listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) along with news articles from specific Twitter handles and proceeds with implementing natural language processing and machine learning methods to filter and retain only relevant articles. A crucial aspect here is the automatic annotation of training data, which can be challenging in many machine learning applications. A Named Entity Recognition model is then used to extract additional relevant information for each article. The data collected over a 1‐month period included 15,088 articles focusing on 585 species listed in Appendix I of CITES. The accuracy of the neural network to detect relevant articles was 95.91% while the Named Entity recognition model helped extract information on prices, location and quantities of traded animals and plants. A regularly updated database, which can be queried and analysed for various research purposes and to inform conservation decision making, is generated by the system. The results demonstrate that natural language processing can be used successfully to extract information from digital text content. The proposed methods can be applied to multiple digital data platforms at the same time and used to investigate human–nature interactions in conservation science and practice.
Highlights
Global biodiversity loss is one of the great sustainability challenges our society is facing (Butchart et al, 2010)
This study demonstrates the potential of using natural language processing and machine learning to identify relevant articles focusing on species of conservation concern and extract relevant information that can be used for further analyses
While in this study we focus on CITES Appendix I listed species and mine data on these species from online news and Twitter, the proposed methods allow for automated content analysis from multiple digital platforms at the same time and for inclusion of a larger number of species globally
Summary
Global biodiversity loss is one of the great sustainability challenges our society is facing (Butchart et al, 2010). In the Information Age, digital data can be leveraged to help address the global biodiversity crisis and study how humans interact with nature (Di Minin et al, 2015; Ladle et al, 2016). Methods for automated content analysis of this deluge of digital data are needed (Di Minin et al, 2019; Lamba et al, 2019; Toivonen et al, 2019). Conservation culturomics is the field of conservation science where digital data sources and methods are being leveraged to help address the global biodiversity crisis and study human–nature interactions (Correia et al, 2021). Digital data sources have the potential to provide information on human–nature interactions at fine spatial and temporal scales (Di Minin et al, 2015). Attention should be paid to ensure responsible use of these data in accordance with data privacy requirements (Di Minin et al, 2021)
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