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The HoneyComb Paradigm for Research on Collective Human Behavior
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Using a cognitive model to understand crowdsourced data from citizen scientists.

Alex Thorpe1, Oliver Kelly2, Alex Callen2

  • 1School of Psychological Sciences, University of Newcastle, Callaghan, Australia.

Behavior Research Methods
|November 29, 2023
PubMed
Summary

Citizen scientists analyzed frog calls from audio recordings. A cultural consensus model accurately assessed their collective findings, validating crowdsourced data for threatened species monitoring without needing prior expert knowledge.

Keywords:
Citizen scienceCrowdsourcingData aggregationData analysis

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Area of Science:

  • Ecology
  • Bioacoustics
  • Conservation Biology

Background:

  • Monitoring threatened species generates vast acoustic and visual data requiring extensive human or machine analysis.
  • Machine learning algorithms for data analysis require large, labeled training datasets, which are often unavailable.
  • Citizen science, or crowdsourcing, offers a scalable solution but faces challenges in data quality assessment due to varying expertise and limited data exposure per participant.

Purpose of the Study:

  • To apply cultural consensus theory, a quantitative cognitive model, to assess the reliability of crowdsourced data from citizen scientists analyzing Australian frog calls.
  • To determine if a consensus model can accurately estimate the competence of citizen scientists and the characteristics of audio recordings, even without a known ground truth.
  • To evaluate the utility of model-based analysis for screening large ecological datasets efficiently.

Main Methods:

  • Utilized cultural consensus theory to analyze both empirical and simulated data from a crowdsourced project involving hundreds of citizen scientists.
  • Citizen scientists were tasked with identifying the presence of nine Australian frog species in 1260 audio recordings.
  • Compared the model's derived consensus with expert evaluations of the same audio recordings.

Main Results:

  • The cultural consensus model successfully estimated characteristics of the citizen scientist cohort and the audio recordings.
  • A significant agreement was found between the consensus derived from the citizen scientist data and the expert coding of the recordings.
  • The model demonstrated that crowdsourced analyses can yield reliable insights even when the ground truth is initially unknown.

Conclusions:

  • Crowdsourced data analysis, when modeled using cultural consensus theory, can be a reliable method for large-scale ecological monitoring.
  • This approach provides a valuable tool for pre-screening large datasets, optimizing the allocation of expert time and resources in conservation efforts.
  • The study validates the use of citizen science and quantitative modeling for effective threatened species monitoring.