the combination of features that best predicted argument strength in this development data, and then used this feature set for the belief change experiments below. Turkers who participated in this initial study did not participate in the belief change study and vice versa.

Results on the development set showed that the z-transformation scores for prior belief and personality performed better than the raw scores and bins. On the other hand, the belief change feature was most effective when represented as a categorical variable via binning and directionality. We also found that it is better to have [**both**]{} the z-transformed prior belief feature and the entrenchment feature. Thus our experiments below use these feature representations.

We test on three different datasets: [mono]{}, [fact]{} and [ emot]{}, to elicit responses from reading the monologic summaries, and the factual and emotional dialogic arguments. The [fact]{} and [ emot]{} datasets have specific information in terms of scalar values about their degree of factuality or emotionality, on a scale of -5,+5 and a feature with this value is created for these datasets derived from the crowdsourced Turker judgments about the degree of Fact/Emotion in a Q/R pair, as described earlier. The monologic summaries ([mono]{}) are 