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.

Absence epilepsy is one of the most common types of epilepsy. The diagnosis of absence epilepsy is among the greatest challenges faced by clinical neurologists due to a lack of easily observable symptoms that are present in conventional epilepsy (e.g. spasm and convulsion), and highly relies on the detection of Spike and Slow Waves (SSWs) in Electroencephalogram (EEG) signals. Recently, graph representations called complex networks have been increasingly applied to characterizing 1D EEG signals. However, existing methods often fail to effectively represent SSWs, struggling to capture the differences between SSW waveforms and their non-SSW counterparts, such as minute differences and distinct shapes. Addressing this issue, in this work, we propose two simple yet effective complex networks, Overlook Graph (OG) and Weighted Overlook Graph (WOG), which have been customized to expressively represent SSWs. Built upon OG and WOG, we then develop a 2D Convolutional Neural Network (2D-CNN) to further learn latent features from the graph representations and accomplish the detection task. Extensive experiments on a real-world absence epilepsy EEG dataset show that the proposed OG/WOG-2D-CNN method can accurately detect SSWs. Additional experiments on the well-known Bonn dataset further show that our method can generalize to the conventional epilepsy seizure detection task with highly competitive performances.