DAPT: Domain-Aware Prompt-Tuning for Multimodal Fake News Detection

摘要

Lately, the academic community has been showing growing interest in multi-domain fake news detection, and in particular, incorporating multimodal information into this field has emerged as a highly promising research direction. However, existing methods often struggle with: (1) Insufficient intrinsic domain adaptation during representation Learning; (2) Amplified negative transfer from entangled domain style and content representations; and (3) Neglecting domain-varying modality uncertainty. To address these issues, we propose Domain-Aware Prompt Tuning (DAPT), an innovative framework for multimodal multi-domain fake news detection. DAPT leverages Multimodal Prompt Tuning for parameter-efficient domain adaptation of pretrain models. An Adaptive Domain Debias Module disentangles domain features from veracity signals guided by content to mitigate negative transfer. Furthermore, inspired by the Variational Information Bottleneck, an Uncertainty-Aware Multimodal Fusion mechanism adaptively aggregates modalities based on domain-specific reliability. Extensive experiments demonstrate that DAPT significantly outperforms state-of-the-art baselines on benchmark datasets.