From Blind Transfer to Wise Selection: Prototype-Driven Neighbor-Domain Adaptation for Fake News Detection

Multimodal fake news detection across different domains is hampered by the critical challenge of negative transfer, which arises from the indiscriminate fusion of knowledge from all available source domains. Existing methods attempt to learn domain-invariant features or leverage external knowledge but often aggregate information from all domains equally. However, these approaches largely ignore the asymmetric relationships between domains, leading to performance degradation when irrelevant or conflicting knowledge is introduced. To address this, we propose a novel PANDA (Prototype-driven Asymmetric Neighbor-Domain Adaptation) framework that dynamically selects and integrates knowledge from only the most beneficial domains. Initially, PANDA employs a Domain-aware Modal Prompt Generation (DMPG) module to learn transferable knowledge representations for each domain. We then introduce a novel Prototype-based Asymmetric Distance (PAD) to quantify directional domain transferability, which guides a Gumbel-based Neighbor Selector (GNS) to identify the most relevant neighbor domains. Subsequently, a Domain-Collaborative Attention (DCA) module adaptively fuses the selected knowledge to enhance the target domain's representation. Extensive experiments on three benchmarks demonstrate PANDA's superiority, outperforming state-of-the-art baselines with an F1-score improvement of 1.5% on the Weibo-21 dataset.