A protocol for harvesting biodiversity data from Facebook

Author:

Chowdhury Shawan1234ORCID,Ahmed Sultan5,Alam Shofiul5ORCID,Callaghan Corey T.6ORCID,Das Priyanka5ORCID,Di Marco Moreno7ORCID,Di Minin Enrico8910ORCID,Jarić Ivan1112ORCID,Labi Mahzabin Muzahid5ORCID,Rokonuzzaman Md.5ORCID,Roll Uri13ORCID,Sbragaglia Valerio14ORCID,Siddika Asma5,Bonn Aletta123ORCID

Affiliation:

1. Institute of Biodiversity Friedrich Schiller University Jena Jena Germany

2. Department of Biodiversity and People Helmholtz Centre for Environmental Research ‐ UFZ Leipzig Germany

3. German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig Germany

4. School of Biological Sciences The University of Queensland Brisbane Queensland Australia

5. Department of Zoology University of Dhaka Dhaka Bangladesh

6. Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center University of Florida Davie Florida USA

7. Department of Biology and Biotechnologies Charles Darwin Sapienza University of Rome Rome Italy

8. Department of Geosciences and Geography University of Helsinki Helsinki Finland

9. Helsinki Institute of Sustainability Science University of Helsinki Helsinki Finland

10. School of Life Sciences University of KwaZulu‐Natal Durban South Africa

11. Université Paris‐Saclay, CNRS, AgroParisTech, Ecologie Systématique Evolution Gif‐sur‐Yvette France

12. Biology Centre of the Czech Academy of Sciences Institute of Hydrobiology České Budějovice Czech Republic

13. Mitrani Department of Desert Ecology, The Jacob Blaustein Institutes for Desert Research Ben‐Gurion University of the Negev Midreshet Ben‐Gurion Israel

14. Department of Marine Renewable Resources Institute of Marine Sciences (ICM‐CSIC) Barcelona Spain

Abstract

AbstractThe expanding use of community science platforms has led to an exponential increase in biodiversity data in global repositories. Yet, understanding of species distributions remains patchy. Biodiversity data from social media can potentially reduce the global biodiversity knowledge gap. However, practical guidelines and standardized methods for harvesting such data are nonexistent. Following data privacy and protection safeguards, we devised a standardized method for extracting species distribution records from Facebook groups that allow access to their data. It involves 3 steps: group selection, data extraction, and georeferencing the record location. We present how to structure keywords, search for species photographs, and georeference localities for such records. We further highlight some challenges users might face when extracting species distribution data from Facebook and suggest solutions. Following our proposed framework, we present a case study on Bangladesh's biodiversity—a tropical megadiverse South Asian country. We scraped nearly 45,000 unique georeferenced records across 967 species and found a median of 27 records per species. About 12% of the distribution data were for threatened species, representing 27% of all species. We also obtained data for 56 DataDeficient species for Bangladesh. If carefully harvested, social media data can significantly reduce global biodiversity knowledge gaps. Consequently, developing an automated tool to extract and interpret social media biodiversity data is a research priority.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Wiley

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