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Online Social Network (OSN) has become a hotbed of fake news due to the lowcost of information dissemination. Although the existing methods have made manyattempts in news content and propagation structure, the detection of fake newsis still facing two challenges: one is how to mine the unique key features andevolution patterns, and the other is how to tackle the problem of small samplesto build the high-performance model. Different from popular methods which takefull advantage of the propagation topology structure, in this paper, we proposea novel framework for fake news detection from perspectives of semantic,emotion and data enhancement, which excavates the emotional evolution patternsof news participants during the propagation process, and a dual deepinteraction channel network of semantic and emotion is designed to obtain amore comprehensive and fine-grained news representation with the considerationof comments. Meanwhile, the framework introduces a data enhancement module toobtain more labeled data with high quality based on confidence which furtherimproves the performance of the classification model. Experiments show that theproposed approach outperforms the state-of-the-art methods.

No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation