Search papers, labs, and topics across Lattice.
This paper introduces CHARM, a novel framework for detecting moral foundations in online endorsement behavior, addressing limitations of existing systems in cross-domain generalization and rationale grounding. By integrating moral grounding, rationale alignment, and hate-speech modulation into a lightweight fine-tuned LLM, CHARM operationalizes distinct psychological constructs to enhance moral prediction. The framework demonstrates significant improvements in AUC and F1 scores across various datasets and reveals a strong correlation between moral value alignment and endorsement behavior in COVID-19 discourse on Twitter.
CHARM not only outperforms existing moral detection systems but also reveals that moral framing significantly influences online endorsement behavior during the COVID-19 pandemic.
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\textbf{C}- and \textbf{H}ate-speech-\textbf{A}ware \textbf{R}ationale-aligned \textbf{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 -- 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.
