Less Is Moral: A CHARMing Framework for Moral Foundations Detection in Endorsement Behaviour

• URL: http://arxiv.org/abs/2609.03330v2
• Date: Mon, 07 Sep 2026 14:04:23 GMT
• Title: Less Is Moral: A CHARMing Framework for Moral Foundations Detection in Endorsement Behaviour
• Abstract summary: Moral language plays a central role in shaping online endorsement and the diffusion of information.<n>We introduce CHARM, a MAC- and Hate-speech-Aware Rationalealigned Moral foundation detection framework.<n>Unlike prior dictionary-, fine-tune-, or prompt-based detectors, CHARM is built so that each component operationalizes a distinct psychological construct.
• Score: 1.3141872672481105
• License: http://creativecommons.org/licenses/by/4.0/
• 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 MAC- and Hate-speech-Aware Rationalealigned Moral 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 -- operationalizes a distinct psychological construct. Using a 30\% subsample of the MFTC, MFRC, and News training pools together with the richer supervision in MFTCXplain, CHARM improves AUC by up to 15.3\% in-domain, surpasses the supervised baselines on every out-of-domain dataset in both AUC and F1, and offers a scalable, low-cost alternative to prompting-based LLM 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. Code and additional materials: https://github.com/HuixiangF/CHARM/.

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