Why are you taking this stance? Identifying and classifying reasons in ideological debates

Recent years have seen a surge of interest in stance classification in online debates. Oftentimes, however, it is important to de-termine not only the stance expressed by an author in her debate posts, but also the reasons behind her supporting or oppos-ing the issue under debate. We therefore examine the new task of reason classifi-cation in this paper. Given the close in-terplay between stance classification and reason classification, we design computa-tional models for examining how automat-ically computed stance information can be profitably exploited for reason classifica-tion. Experiments on our reason-annotated corpus of ideological debate posts from four domains demonstrate that sophisti-cated models of stances and reasons can indeed yield more accurate reason and stance classification results than their sim-pler counterparts.