# Measuring Moral Dimensions in Social Media with Mformer

arXiv: https://arxiv.org/abs/2311.10219 · HTML: https://mlsanity.com/paper/2311.10219/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2023.

## Built on by (most cited first, 13 of 13)

- [MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions](https://mlsanity.com/paper/2403.07678/) (2024)
- [MoVa: Towards Generalizable Classification of Human Morals and Values](https://mlsanity.com/paper/2509.24216/) (2025)
- [Tracing Moral Foundations in Large Language Models](https://mlsanity.com/paper/2601.05437/) (2026)
- [A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challenges](https://mlsanity.com/paper/2409.13521/) (2024)
- [Beyond Human Judgment: A Bayesian Evaluation of LLMs' Moral Values Understanding](https://mlsanity.com/paper/2508.13804/) (2025)
- [Communication styles and reader preferences of LLM- and human-authored COVID-19 information explanations: a case study](https://mlsanity.com/paper/2505.08143/) (2025)
- [The Moral Gap of Large Language Models](https://mlsanity.com/paper/2507.18523/) (2025)
- [Predicting User Stances from Target-Agnostic Information using Large Language Models](https://mlsanity.com/paper/2409.14395/) (2024)
- [Beyond English: Evaluating Automated Measurement of Moral Foundations in Non-English Discourse with a Chinese Case Study](https://mlsanity.com/paper/2502.02451/) (2025)
- [Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions](https://mlsanity.com/paper/2604.21871/) (2026)
- [Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corpora](https://mlsanity.com/paper/2605.22660/) (2026)
- [MoralCLIP: Contrastive Alignment of Vision-and-Language Representations with Moral Foundations Theory](https://mlsanity.com/paper/2506.05696/) (2025)
- [Learning Moral Diversity: Modelling Individual Perspectives in Moral Classification of Texts](https://mlsanity.com/paper/2606.22771/) (2026)

## Abstract (arXiv)

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.