Go to DBLP homepageA Multi-modal Prompt Learning Framework for Early Detection of Fake News
Abstract: Information spreads quickly through social media platforms, especially fake news with negative or even malicious intentions. In recent years, psychological studies have found that explicit reminders of fake news would diminish its consequence. Therefore, it is crucial to identify their authenticity at an early stage to avoid serious consequences. However, existing methods for fake news detection either utilize auxiliary information including users’ profiles and related events propagation networks or require sufficient and high-quality training data, which is not suitable for early fake news detection in real. An increasing number of social media news not only involves natural language content but also visual content such as images and videos, which give us a new view of fake news detection at an early stage by multi-modal data. In this paper, we propose a Multi-modal Prompt Learning framework (MPL) based on the multi-modal pre-trained model CLIP for early detection of fake news. A learnable prompt module is developed to adaptively and efficiently generate prompt representations to boost the semantic context. MPL can be implemented in supervised or few-shot settings. Extensive experiments show that the proposed MPL obtains substantial performance and efficiency improvement for the early-stage fake news detection task. The results demonstrate that MPL performs considerably well compared to both the state-ofthe-art supervised multi-modal models and the latest promptbased few-shot multi-modal models. Especially, the high recall of fake news and the high precision of real news that MPL achieved compared to other baselines verify that it will better approach one of the motivations that providing early notification of “maybe real” or “maybe fake” with the release of the news.
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