Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

9-2024

Abstract

This study enhances stance detection on social media by incorporating deeper psychological attributes, specifically individuals’ moral foundations. These theoretically-derived dimensions aim to provide an interpretable 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 detecting an individual’s stance on a given target. Specifically, we incorporate moral foundation features extracted from text, along with semantic features, to classify stances at both message-and user-levels using traditional machine learning and Large Language Models (LLMs). Our preliminary results suggest that encoding moral foundations can enhance the performance of stance detection tasks, but with notable heterogeneity across task type, models, and datasets. In addition, we illustrate meaningful associations between specific moral foundations and online stances on target topics. The findings from this study highlight the importance of considering deeper psychological attributes in stance classification tasks, and underscore the role of moral foundations in guiding online social behavior.

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

Proceedings of the 16th International Conference on Advances in Social Networks Analysis and Mining, Italy, September 2-5

First Page

1

Last Page

8

Publisher

Springer

City or Country

Cham

Comments

The paper has been presented in the conference. The official conference proceedings have not been online yet.

Additional URL

https://arxiv.org/abs/2310.09848

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