Modelling influence and opinion evolution in online collective behaviour

TL;DR

This study introduces a validated consensus model for predicting opinion evolution in online collective behavior, using real-world data and accounting for individual influenceability and unpredictability in judgment revision.

Contribution

It is the first to test a quantitative opinion dynamics model's predictive power against real data without calibration bias, highlighting the role of unpredictability.

Findings

Over two thirds of prediction errors are due to human unpredictability.

Model accuracy improves with prior knowledge of individual behavior.

Prediction is limited by inherent unpredictability in judgment revision.

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.