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• W2263702930 startingPage "e0157685" @default.
• W2263702930 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." @default.
• W2263702930 created "2016-06-24" @default.
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• W2263702930 date "2016-06-23" @default.
• W2263702930 modified "2023-10-09" @default.
• W2263702930 title "Modelling Influence and Opinion Evolution in Online Collective Behaviour" @default.
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• W2263702930 doi "https://doi.org/10.1371/journal.pone.0157685" @default.
• W2263702930 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4918933" @default.
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