Kavli Affiliate: Wei Gao
| First 5 Authors: Hong Zhang, Quoc-Nam Nguyen, Prasanta Bhattacharya, Wei Gao, Liang Ze Wong
| Summary:
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
using both traditional machine learning models and large language 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.
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