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Increasing biodiversity knowledge through social media: A case study from tropical Bangladesh.

Shawan ChowdhuryUpama AichMd RokonuzzamanShofiul AlamPriyanka DasAsma SiddikaSultan AhmedMahzabin Muzahid LabiMoreno Di MarcoRichard A FullerCorey T Callaghan
Published in: Bioscience (2023)
Citizen science programs are becoming increasingly popular among naturalists but remain heavily biased taxonomically and geographically. However, with the explosive popularity of social media and the near-ubiquitous availability of smartphones, many post wildlife photographs on social media. Here, we illustrate the potential of harvesting these data to enhance our biodiversity understanding using Bangladesh, a tropical biodiverse country, as a case study. We compared biodiversity records extracted from Facebook with those from the Global Biodiversity Information Facility (GBIF), collating geospatial records for 1013 unique species, including 970 species from Facebook and 712 species from GBIF. Although most observation records were biased toward major cities, the Facebook records were more evenly spatially distributed. About 86% of the Threatened species records were from Facebook, whereas the GBIF records were almost entirely Of Least Concern species. To reduce the global biodiversity data shortfall, a key research priority now is the development of mechanisms for extracting and interpreting social media biodiversity data.
Keyphrases
  • social media
  • health information
  • electronic health record
  • public health
  • big data
  • climate change
  • healthcare
  • machine learning
  • risk assessment
  • deep learning
  • long term care