Crowdsourcing without a crowd: Reliable online species identification using Bayesian models to minimize crowd size

Advaith Siddharthan, Christopher Lambin, Anne-Marie Robinson, Nirwan Sharma, Richard Comont, Elaine O'Mahony, Chris Mellish, Rene Van Der Wal

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
14 Downloads (Pure)


We present an incremental Bayesian model which resolves key issues of crowd size and data quality for consensus labelling. We evaluate our method using data collected from a real world citizen science program, BEEWATCH, which invites members of the public in the UK to classify (label) photographs of bumblebees
as one of 22 possible species. The biological recording domain poses two key and hitherto unaddressed challenges for consensus models of crowdsourcing: (a) the large number of potential species makes classification difficult and (b) this is compounded by limited crowd availability, stemming from both the inherent difficulty of the task and the lack of relevant skills among the general public. We demonstrate that consensus labels can be reliably found in such circumstances with very small crowd sizes of around 3–5 users (i.e. through group sourcing). Our incremental Bayesian model, which minimizes crowd size by re-evaluating the quality of the consensus label following each species identification solicited from the crowd, is competitive with a Bayesian approach that uses a larger but fixed crowd size and outperforms majority voting. These results have important ecological applicability: biological recording programs such as BEEWATCH can sustain themselves when resources such as taxonomic experts to confirm identifications by photo submitters are scarce (as is typically the case), and feedback can be provided to submitters in a timely fashion. More generally,
our model provides benefits to any crowdsourced consensus labeling task where there is a cost (financial or otherwise) associated with soliciting a label.
Original languageEnglish
Article number45
Pages (from-to)1-20
Number of pages20
JournalACM Transactions on Intelligent Systems and Technology
Issue number4
Early online date1 May 2016
Publication statusPublished - 14 Jul 2016

Bibliographical note

This research was supported by an award made by the RCUK Digital Economy program to the University of Aberdeen’s dot.rural Digital Economy Hub (ref. EP/G066051/1). Christopher Lambin was funded through a NERC research experience placements grant.


  • crowdsourcing
  • citizen science
  • consensus model
  • Bayesian reasoning
  • bumblebee identification
  • biological recording


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