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Modelling Influence and Opinion Evolution in Online Collective Behaviour

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  • Corentin Vande Kerckhove
  • Samuel Martin
  • Pascal Gend
  • Peter J Rentfrow
  • Julien M Hendrickx
  • Vincent D Blondel

Abstract

Opinion evolution and judgment revision are mediated through social influence. Based on a large crowdsourced in vitro experiment (n = 861), it is shown how a consensus model can be used to predict opinion evolution in online collective behaviour. It is the first time the predictive power of a quantitative model of opinion dynamics is tested against a real dataset. Unlike previous research on the topic, the model was validated on data which did not serve to calibrate it. This avoids to favor more complex models over more simple ones and prevents overfitting. The model is parametrized by the influenceability of each individual, a factor representing to what extent individuals incorporate external judgments. The prediction accuracy depends on prior knowledge on the participants’ past behaviour. Several situations reflecting data availability are compared. When the data is scarce, the data from previous participants is used to predict how a new participant will behave. Judgment revision includes unpredictable variations which limit the potential for prediction. A first measure of unpredictability is proposed. The measure is based on a specific control experiment. More than two thirds of the prediction errors are found to occur due to unpredictability of the human judgment revision process rather than to model imperfection.

Suggested Citation

  • Corentin Vande Kerckhove & Samuel Martin & Pascal Gend & Peter J Rentfrow & Julien M Hendrickx & Vincent D Blondel, 2016. "Modelling Influence and Opinion Evolution in Online Collective Behaviour," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-25, June.
    • Handle: RePEc:plo:pone00:0157685
    DOI: 10.1371/journal.pone.0157685
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    References listed on IDEAS

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    Cited by:

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    2. Bertrand Jayles & Clément Sire & Ralf H J M Kurvers, 2021. "Crowd control: Reducing individual estimation bias by sharing biased social information," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-28, November.
    3. Shane T. Mueller & Yin-Yin Sarah Tan, 2018. "Cognitive perspectives on opinion dynamics: the role of knowledge in consensus formation, opinion divergence, and group polarization," Journal of Computational Social Science, Springer, vol. 1(1), pages 15-48, January.
    4. I. C. Morarescu & V. S. Varma & L. Busoniu & S. Lasaulce, 2020. "Space-time budget allocation policy design for viral marketing," Papers 2011.08639, arXiv.org.
    5. Jayles, Bertrand & Escobedo, Ramon & Cezera, Stéphane & Blanchet, Adrien & Kameda, Tatsuya & Sire, Clément & Théraulaz, Guy, 2020. "The impact of incorrect social information on collective wisdom in human groups," IAST Working Papers 20-106, Institute for Advanced Study in Toulouse (IAST).

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