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A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

Author

Listed:
  • Jesus Cerquides

    (Institut d’Investigació en Intel ligència Artificial (IIIA), CSIC, 08193 Cerdanyola, Spain)

  • Mehmet Oğuz Mülâyim

    (Institut d’Investigació en Intel ligència Artificial (IIIA), CSIC, 08193 Cerdanyola, Spain)

  • Jerónimo Hernández-González

    (Department de Matemàtiques, Universitat de Barcelona, 08007 Barcelona, Spain)

  • Amudha Ravi Shankar

    (Citizen Cyberlab, CUI, University of Geneva, CH-1227 Geneva, Switzerland)

  • Jose Luis Fernandez-Marquez

    (Citizen Cyberlab, CUI, University of Geneva, CH-1227 Geneva, Switzerland)

Abstract

Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led to publications in major scientific journals. However, little attention has been paid to data quality issues. In this work we argue that being able to determine the accuracy of data obtained by crowdsourcing is a fundamental question and we point out that, for many real-life scenarios, mathematical tools and processes for the evaluation of data quality are missing. We propose a probabilistic methodology for the evaluation of the accuracy of labeling data obtained by crowdsourcing in citizen science. The methodology builds on an abstract probabilistic graphical model formalism, which is shown to generalize some already existing label aggregation models. We show how to make practical use of the methodology through a comparison of data obtained from different citizen science communities analyzing the earthquake that took place in Albania in 2019.

Suggested Citation

  • Jesus Cerquides & Mehmet Oğuz Mülâyim & Jerónimo Hernández-González & Amudha Ravi Shankar & Jose Luis Fernandez-Marquez, 2021. "A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data," Mathematics, MDPI, vol. 9(8), pages 1-15, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:875-:d:536894
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    References listed on IDEAS

    as
    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Andrei P. Kirilenko & Travis Desell & Hany Kim & Svetlana Stepchenkova, 2017. "Crowdsourcing Analysis of Twitter Data on Climate Change: Paid Workers vs. Volunteers," Sustainability, MDPI, vol. 9(11), pages 1-15, November.
    3. Trisha Gura, 2013. "Citizen science: Amateur experts," Nature, Nature, vol. 496(7444), pages 259-261, April.
    4. A. P. Dawid & A. M. Skene, 1979. "Maximum Likelihood Estimation of Observer Error‐Rates Using the EM Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 20-28, March.
    Full references (including those not matched with items on IDEAS)

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