DAFT: Domain-Augmented Fine-Tuning for Large Language Models in Emotion Recognition of Health Misinformation

This study proposes a domain-augmented fine-tuning strategy for improving emotion recognition in health misinformation using pre-trained large language models (LLMs). The proposed method aims to address key limitations of existing approaches, including insufficient precision, weak domain adaptability, and low recognition accuracy for complex emotional expressions in health-related misinformation. Specifically, the Domain-Augmented Fine-Tuning (DAFT) method extends a health emotion lexicon to annotate emotion-oriented corpora, designs task-specific prompt templates to enhance semantic understanding, and fine-tunes GPT-based LLMs through parameter-efficient prompt tuning. Empirical experiments conducted on a health misinformation dataset demonstrate that DAFT substantially improves model performance in terms of prediction error, emotional vector structural similarity, probability distribution consistency, and classification accuracy. The fine-tuned GPT-4o model achieves the best overall performance, attaining an emotion recognition accuracy of 84.77%, with its F1-score increasing by 20.78% relative to the baseline model. Nonetheless, the corpus constructed in this study is based on a six-dimensional emotion framework, which may not fully capture nuanced emotions in complex linguistic contexts. Moreover, the dataset is limited to textual information, and future research should incorporate multimodal data such as images and videos. Overall, the DAFT method effectively enhances the domain adaptability of LLMs and provides a lightweight yet efficient approach to emotion recognition in health misinformation scenarios.