MT-GPD: A Multimodal Deep Transfer Learning Model Enhanced by Auxiliary Mechanisms for Cross-Domain Online Fake News Detection

The proliferation of fake news, more recently multimodal fake news, poses a significant threat to individuals, organizations, and society. While online social media platforms have employed automated methods to combat fake news, they face two notable challenges: the scarcity of labeled data and the diversity of news domains. To enhance the effectiveness and efficiency of online platforms in mitigating the spread of fake news, this study proposes MT-GPD (multimodal deep transfer learning with gating network, model patch, and domain classifier) for cross-domain fake news detection. MT-GPD integrates three novel design artifacts as auxiliary mechanisms for enhancing multimodal deep transfer learning, including a gating network that captures the relative importance of textual and visual components of individual news articles for dynamic fusion; a customized model patch that balances detection performance and computational efficiency; and a domain classifier that adapts multimodal representations to a target news domain. We evaluate the performance of MT-GPD using news datasets spanning four different domains. The results demonstrate the efficacy and robustness of MT-GPD, providing strong evidence for the impacts of the proposed auxiliary mechanisms on improving fake news detection performance.