# Learning Moral Diversity: Modelling Individual Perspectives in Moral Classification of Texts

arXiv: https://arxiv.org/abs/2606.22771 · HTML: https://mlsanity.com/paper/2606.22771/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [Deep learning from crowds](https://mlsanity.com/paper/1709.01779/) (2017)
- [Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions](https://mlsanity.com/paper/2502.20897/) (2025)
- [MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction](https://mlsanity.com/paper/1904.08314/) (2019)
- [Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks](https://mlsanity.com/paper/2311.09743/) (2023)
- [MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions](https://mlsanity.com/paper/2403.07678/) (2024)
- [Acquiring Background Knowledge to Improve Moral Value Prediction](https://mlsanity.com/paper/1709.05467/) (2017)
- [A Data Fusion Framework for Multi-Domain Morality Learning](https://mlsanity.com/paper/2304.02144/) (2023)
- [Measuring Moral Dimensions in Social Media with Mformer](https://mlsanity.com/paper/2311.10219/) (2023)
- [Learning to Adapt Domain Shifts of Moral Values via Instance Weighting](https://mlsanity.com/paper/2204.07603/) (2022)
- [Moral Narratives Around the Vaccination Debate on Facebook](https://mlsanity.com/paper/2206.01598/) (2022)
- [Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot Annotator Adaptation](https://mlsanity.com/paper/2402.14101/) (2024)
- [Moral Values Underpinning COVID-19 Online Communication Patterns](https://mlsanity.com/paper/2401.08789/) (2024)

## Abstract (arXiv)

Understanding moral values in social media text offers insight into moral judgement formation, and supervised NLP models trained on crowdsourced data have achieved strong classification performance. However, most approaches simplify the problem by aggregating multiple annotators' labels into a single "ground truth", overlooking the inherent subjectivity of the task. In practice, there are disagreements between annotators caused by personal viewpoint or inherent ambiguities, particularly for short tweets. Here, we extend a pretrained language model with a layer that learns annotator-specific features. Our model improves predictions of individual annotations and yields representations that reveal meaningful insights into annotators' moral perspectives. We show that models trained on aggregated labels may hide variation and give a misleading impression of performance. Overall, we demonstrate that disagreement reflects the inherent subjectivity of the task and that modelling individual perspectives creates benefits for moral classification of texts.