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A protocol for harvesting biodiversity data from Facebook.

Shawan Chowdhury^1,2,3,4, Sultan Ahmed⁵, Shofiul Alam⁵

¹Institute of Biodiversity, Friedrich Schiller University Jena, Jena, Germany.

Conservation Biology : the Journal of the Society for Conservation Biology

|March 28, 2024

Summary

This summary is machine-generated.

Social media platforms can help fill biodiversity knowledge gaps by providing valuable species distribution data. A new standardized method for extracting this data from Facebook groups offers a practical solution for researchers.

Keywords:

Bangladesh Facebook Wallacean shortfall ciencia ciudadana citizen science colaboración masiva crowdsourcing déficit wallaceano iEcology megadiverse countries países megadiversos redes sociales social media tropics trópicos

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Area of Science:

Biodiversity research
Citizen science
Data science

Background:

Community science platforms have increased biodiversity data, but species distribution knowledge remains incomplete.
Social media data offers a potential solution to bridge global biodiversity knowledge gaps.
Existing methods for harvesting social media biodiversity data lack standardization.

Purpose of the Study:

To devise and present a standardized method for extracting species distribution records from accessible Facebook groups.
To address the lack of practical guidelines for utilizing social media data in biodiversity research.
To demonstrate the utility of this method through a case study in Bangladesh.

Main Methods:

A 3-step framework was developed: group selection, data extraction, and georeferencing.
The method involves structuring keywords, searching for species photographs, and georeferencing locations.
Data privacy and protection safeguards were followed throughout the extraction process.

Main Results:

Nearly 45,000 unique georeferenced records for 967 species were extracted from Bangladesh Facebook groups.
A median of 27 records per species was observed.
12% of the data pertained to threatened species, and data for 56 Data Deficient species were obtained.

Conclusions:

Social media data, when carefully harvested using standardized methods, can significantly reduce global biodiversity knowledge gaps.
The proposed framework provides a practical approach for researchers to access valuable biodiversity information.
Developing automated tools for social media biodiversity data extraction and interpretation is a critical research priority.