  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.
