Automated retrieval of information on threatened species from online sources using machine learning

Abstract

As resources for conservation are limited, gathering and analyzing 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.2. In this study, we introduce novel application methods to automatically collect and analyze 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.3. The data collected over a one 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. 4. 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.

Quantitative conservation geography

“…OPEN ACCESS images and video using automated content analysis [78,81,82] (Figure 2). Images and text collected from digital platforms could be, for example, used to assess the views, values, and preferences of multiple stakeholders [83].…”

Integração e Rotulação Automatizada de Dados sobre o Cnidário Physalia physalis, usando a Geolocalização como Referência

“…Os autores [Kulkarni and Di Minin 2021] construíram um pipeline, com destaque para métodos de coleta e análise de dados de forma automatizada. Entre os métodos, os autores trabalham na deduplicac ¸ão de dados e na integrac ¸ão de dados semelhantes.…”

“…De forma automatizada, os dados serão combinados e agrupados quando estes estiverem em fontes diferentes, mas possuírem o mesmo intervalo de tempo e a mesma localizac ¸ão. Para esta automatizac ¸ão, pretende-se utilizar técnicas de Reconhecimento de Entidades Nomeadas e de vetorizac ¸ão de textos, como descritas em [Kulkarni and Di Minin 2021].…”

Automated synthesis of biodiversity knowledge requires better tools and standardised research output

“…Complementing these broader, summarisation approaches, direct extraction of ecologically valuable information from literature (e.g. species names and geographic locations) is a growing field, with recent examples including Akella et al (2012), Millard et al (2020 and Kulkarni and Di Minin (2021).…”