Measuring Moral Dimensions in Social Media with Mformer

TL;DR

This paper introduces Mformer, a fine-tuned large language model that accurately detects moral foundations in social media texts, outperforming existing tools and aiding in understanding moral debates online.

Contribution

The paper presents Mformer, a novel large language model fine-tuned for moral foundation detection, with improved accuracy and generalization across diverse social media datasets.

Findings

Mformer outperforms existing methods by 4-12% in AUC on the same domains.

It generalizes well to new datasets, with up to 17% AUC improvement.

Case studies demonstrate its effectiveness in analyzing moral stances on social issues.

Abstract

The ever-growing textual records of contemporary social issues, often discussed online with moral rhetoric, present both an opportunity and a challenge for studying how moral concerns are debated in real life. Moral foundations theory is a taxonomy of intuitions widely used in data-driven analyses of online content, but current computational tools to detect moral foundations suffer from the incompleteness and fragility of their lexicons and from poor generalization across data domains. In this paper, we fine-tune a large language model to measure moral foundations in text based on datasets covering news media and long- and short-form online discussions. The resulting model, called Mformer, outperforms existing approaches on the same domains by 4--12% in AUC and further generalizes well to four commonly used moral text datasets, improving by up to 17% in AUC. We present case studies using Mformer to analyze everyday moral dilemmas on Reddit and controversies on Twitter, showing that moral foundations can meaningfully describe people's stance on social issues and such variations are topic-dependent. Pre-trained model and datasets are released publicly. We posit that Mformer will help the research community quantify moral dimensions for a range of tasks and data domains, and eventually contribute to the understanding of moral situations faced by humans and machines.