A protocol for harvesting biodiversity data from Facebook

10 pages, 4 figures, supporting Information https://doi.org/10.1111/cobi.14257.-- Data Availability: This article has earned Open Data and Open Materials badges. Data and materials are available at https://doi.pangaea.de/10.1594/PANGAEA.948104

The 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

S.C. and A.B. gratefully acknowledge the support of the German Centre for Integrative Biodiversity Research (iDiv) and the sMon project funded by the German Research Foundation (DFG-FZT 118, 202548816). VS is supported by a Ramón y Cajal research fellowship (RYC2021-033065-I) granted by the Spanish Ministry of Science and Innovation and he also acknowledges the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accredition (CEX2019-000928-S)

Peer reviewed