Enhancing Stance Classification with Quantified Moral Foundations
Hong Zhang Prasanta Bhattacharya Wei Gao Liang Ze Wong Brandon Siyuan Loh Joseph J. P. Simons Jisun An
This study enhances stance detection on social media by incorporating deeper
psychological attributes, specifically individuals' moral foundations. These
theoretically-derived dimensions aim to provide a comprehensive profile of an
individual's moral concerns which, in recent work, has been linked to behaviour
in a range of domains, including society, politics, health, and the
environment. In this paper, we investigate how moral foundation dimensions can
contribute to predicting an individual's stance on a given target. Specifically
we incorporate moral foundation features extracted from text, along with
message semantic features, to classify stances at both message- and user-levels
across a range of targets and models. Our preliminary results suggest that
encoding moral foundations can enhance the performance of stance detection
tasks and help illuminate the associations between specific moral foundations
and online stances on target topics. The results highlight the importance of
considering deeper psychological attributes in stance analysis and underscores
the role of moral foundations in guiding online social behavior.