human subjects. We use the {[mono]{}, [fact]{}, [ emot]{}}+None feature sets for testing our hypothesis that personality affects belief change, and [**our ability to predict belief change**]{} using personality features. [*All*]{} feature sets have information about [**all**]{} of the human subjects’ personality traits as 5 distinct features. The remaining five [*{O,C,E,A,N}*]{} feature sets examine the effect of providing information to the learner about personality using only [**one personality trait at a time**]{}, in order to determine if any personality trait is having a larger impact for belief change prediction.

Table \[tab:belief\_change\] summarizes our key results, reporting accuracy, precision, recall, and F1 for predicting belief change as a discrete bin, Low, Medium, and High. We balanced each dataset to contain the same number of instances in bins, thus the accuracy for majority classification is 33% (Row 1).

After running Naive Bayes over all feature sets in the three datasets, we compared the experimental classifier performance of [*All*]{} and [*{O,C,E,A,N}*]{} against the None baselines using a Bonferroni corrected t-test for F1 measure. Using statistical [anova]{} tests that control for pre and post test sample variance, we found small but highly reliable effects. We show all of our results, but focus our discussion 