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.

翻译：本研究通过融入更深层次的心理属性——特别是用户的道德基础——来增强社交媒体上的立场检测。这些理论推导的维度旨在全面刻画个体的道德关切，近期研究已将其与社会、政治、健康及环境等多个领域的行为联系起来。本文探讨了道德基础维度如何有助于预测个体对特定目标的立场。具体而言，我们结合从文本中提取的道德基础特征与消息语义特征，分别使用传统机器学习模型和大语言模型在消息层面和用户层面进行立场分类。初步结果表明，编码道德基础能够提升立场检测任务的性能，并有助于揭示特定道德基础与针对目标话题的在线立场之间的关联。这些发现凸显了在立场分析中考虑深层心理属性的重要性，同时强调了道德基础在引导在线社会行为中的作用。