Literature DB >> 33749027

Digital data sources and methods for conservation culturomics.

Ricardo A Correia1,2,3,4, Richard Ladle4,5, Ivan Jarić6,7, Ana C M Malhado4, John C Mittermeier8, Uri Roll9, Andrea Soriano-Redondo5,10, Diogo Veríssimo11,12,13, Christoph Fink1,2, Anna Hausmann1,2, Jhonatan Guedes-Santos4, Reut Vardi14, Enrico Di Minin1,2,15.   

Abstract

Ongoing loss of biological diversity is primarily the result of unsustainable human behavior. Thus, the long-term success of biodiversity conservation depends on a thorough understanding of human-nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. However, the Information Age also provides new opportunities to better understand human-nature interactions because many aspects of daily life are recorded in a variety of digital formats. The emerging field of conservation culturomics aims to take advantage of digital data sources and methods to study human-nature interactions and thus to provide new tools for studying conservation at relevant temporal and spatial scales. Nevertheless, technical challenges associated with the identification, access, and analysis of relevant data hamper the wider adoption of culturomics methods. To help overcome these barriers, we propose a conservation culturomics research framework that addresses data acquisition, analysis, and inherent biases. The main sources of culturomic data include web pages, social media, and other digital platforms from which metrics of content and engagement can be obtained. Obtaining raw data from these platforms is usually desirable but requires careful consideration of how to access, store, and prepare the data for analysis. Methods for data analysis include network approaches to explore connections between topics, time-series analysis for temporal data, and spatial modeling to highlight spatial patterns. Outstanding challenges associated with culturomics research include issues of interdisciplinarity, ethics, data biases, and validation. The practical guidance we offer will help conservation researchers and practitioners identify and obtain the necessary data and carry out appropriate analyses for their specific questions, thus facilitating the wider adoption of culturomics approaches for conservation applications.
© 2021 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.

Entities:  

Keywords:  ciencia guiada por datos; contenido digital; data-driven science; digital content; digital methods; human-nature interactions; interacciones humano-naturaleza; marco de trabajo de investigación; métodos digitales; research framework; 人与自然的互动; 数字内容; 数字方法; 数据驱动的科学; 研究框架

Mesh:

Year:  2021        PMID: 33749027     DOI: 10.1111/cobi.13706

Source DB:  PubMed          Journal:  Conserv Biol        ISSN: 0888-8892            Impact factor:   6.560


  6 in total

1.  Environmental Discourse Exhibits Consistency and Variation across Spatial Scales on Twitter.

Authors:  Charlotte H Chang; Paul R Armsworth; Yuta J Masuda
Journal:  Bioscience       Date:  2022-07-13       Impact factor: 11.566

Review 2.  Past and future uses of text mining in ecology and evolution.

Authors:  Maxwell J Farrell; Liam Brierley; Anna Willoughby; Andrew Yates; Nicole Mideo
Journal:  Proc Biol Sci       Date:  2022-05-18       Impact factor: 5.530

3.  Viewing the rare through public lenses: insights into dead calf carrying and other thanatological responses in Asian elephants using YouTube videos.

Authors:  Sanjeeta Sharma Pokharel; Nachiketha Sharma; Raman Sukumar
Journal:  R Soc Open Sci       Date:  2022-05-18       Impact factor: 3.653

4.  Evaluating the reliability of media reports for gathering information about illegal wildlife trade seizures.

Authors:  Kumar Paudel; Amy Hinsley; Diogo Veríssimo; Ej Milner-Gulland
Journal:  PeerJ       Date:  2022-04-05       Impact factor: 2.984

5.  Public's Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques.

Authors:  Saad Awadh Alanazi; Ayesha Khaliq; Fahad Ahmad; Nasser Alshammari; Iftikhar Hussain; Muhammad Azam Zia; Madallah Alruwaili; Alanazi Rayan; Ahmed Alsayat; Salman Afsar
Journal:  Int J Environ Res Public Health       Date:  2022-08-06       Impact factor: 4.614

6.  Framing of visual content shown on popular social media may affect viewers' attitudes to threatened species.

Authors:  Fernando Ballejo; Pablo Ignacio Plaza; Sergio Agustín Lambertucci
Journal:  Sci Rep       Date:  2021-06-29       Impact factor: 4.379

  6 in total

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