@inproceedings{fu-rizoiu-2026-less,
title = "Less Is Moral: A {CHARM}ing Framework for Moral Foundations Detection in Endorsement Behaviour",
author = "Fu, Huixiang and
Rizoiu, Marian-Andrei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.912/",
pages = "21003--21025",
ISBN = "979-8-89176-459-0",
abstract = "Moral language plays a central role in shaping online endorsement and the diffusion of information, yet existing moral foundation detection systems often suffer from poor cross-domain generalization, weak rationale grounding, and reliance on costly prompting-based large language models (LLMs). We introduce CHARM, a MA**C** and **H**ate-speech-**A**ware **R**ationale-aligned **M**oral foundation detection framework built on a lightweight fine-tuned LLM, which integrates complementary moral grounding, rationale alignment, and polarity-aware hate speech signals to support more robust and faithful moral prediction. Unlike prior dictionary-, fine-tune-, or prompt-based detectors, which decouple computation from psychological theory, CHARM is built so that each component {---} MAC cross-attention, rationale alignment, and hate-speech modulation {---} operationalises a distinct psychological construct. Using only 30{\%} of the training data required by prior fine-tuned baselines, CHARM improves AUC by up to 15.3{\%} in-domain, surpasses these baselines on every out-of-domain dataset in both AUC and F1, and offers a scalable, low-cost alternative to LLM-based detectors. We further apply CHARM to large-scale COVID-19 discourse on Twitter and show that moral value alignment is strongly associated with online endorsement behavior. By making moral framing measurable at scale, CHARM offers a practical tool for studying the spread of morally charged misinformation."
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<abstract>Moral language plays a central role in shaping online endorsement and the diffusion of information, yet existing moral foundation detection systems often suffer from poor cross-domain generalization, weak rationale grounding, and reliance on costly prompting-based large language models (LLMs). We introduce CHARM, a MA**C** and **H**ate-speech-**A**ware **R**ationale-aligned **M**oral foundation detection framework built on a lightweight fine-tuned LLM, which integrates complementary moral grounding, rationale alignment, and polarity-aware hate speech signals to support more robust and faithful moral prediction. Unlike prior dictionary-, fine-tune-, or prompt-based detectors, which decouple computation from psychological theory, CHARM is built so that each component — MAC cross-attention, rationale alignment, and hate-speech modulation — operationalises a distinct psychological construct. Using only 30% of the training data required by prior fine-tuned baselines, CHARM improves AUC by up to 15.3% in-domain, surpasses these baselines on every out-of-domain dataset in both AUC and F1, and offers a scalable, low-cost alternative to LLM-based detectors. We further apply CHARM to large-scale COVID-19 discourse on Twitter and show that moral value alignment is strongly associated with online endorsement behavior. By making moral framing measurable at scale, CHARM offers a practical tool for studying the spread of morally charged misinformation.</abstract>
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%0 Conference Proceedings
%T Less Is Moral: A CHARMing Framework for Moral Foundations Detection in Endorsement Behaviour
%A Fu, Huixiang
%A Rizoiu, Marian-Andrei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F fu-rizoiu-2026-less
%X Moral language plays a central role in shaping online endorsement and the diffusion of information, yet existing moral foundation detection systems often suffer from poor cross-domain generalization, weak rationale grounding, and reliance on costly prompting-based large language models (LLMs). We introduce CHARM, a MA**C** and **H**ate-speech-**A**ware **R**ationale-aligned **M**oral foundation detection framework built on a lightweight fine-tuned LLM, which integrates complementary moral grounding, rationale alignment, and polarity-aware hate speech signals to support more robust and faithful moral prediction. Unlike prior dictionary-, fine-tune-, or prompt-based detectors, which decouple computation from psychological theory, CHARM is built so that each component — MAC cross-attention, rationale alignment, and hate-speech modulation — operationalises a distinct psychological construct. Using only 30% of the training data required by prior fine-tuned baselines, CHARM improves AUC by up to 15.3% in-domain, surpasses these baselines on every out-of-domain dataset in both AUC and F1, and offers a scalable, low-cost alternative to LLM-based detectors. We further apply CHARM to large-scale COVID-19 discourse on Twitter and show that moral value alignment is strongly associated with online endorsement behavior. By making moral framing measurable at scale, CHARM offers a practical tool for studying the spread of morally charged misinformation.
%U https://aclanthology.org/2026.emnlp-main.912/
%P 21003-21025
Markdown (Informal)
[Less Is Moral: A CHARMing Framework for Moral Foundations Detection in Endorsement Behaviour](https://aclanthology.org/2026.emnlp-main.912/) (Fu & Rizoiu, EMNLP 2026)
ACL