No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation
This work addresses the problem of detecting fake news in online social networks, which is an incremental improvement over existing methods by focusing on emotional patterns and data augmentation.
The paper tackles fake news detection by proposing a dual deep interaction channel network that integrates semantic and emotional evolution patterns from news comments, along with a data augmentation module to address small sample sizes, achieving state-of-the-art performance.
Online Social Network (OSN) has become a hotbed of fake news due to the low cost of information dissemination. Although the existing methods have made many attempts in news content and propagation structure, the detection of fake news is still facing two challenges: one is how to mine the unique key features and evolution patterns, and the other is how to tackle the problem of small samples to build the high-performance model. Different from popular methods which take full advantage of the propagation topology structure, in this paper, we propose a novel framework for fake news detection from perspectives of semantic, emotion and data enhancement, which excavates the emotional evolution patterns of news participants during the propagation process, and a dual deep interaction channel network of semantic and emotion is designed to obtain a more comprehensive and fine-grained news representation with the consideration of comments. Meanwhile, the framework introduces a data enhancement module to obtain more labeled data with high quality based on confidence which further improves the performance of the classification model. Experiments show that the proposed approach outperforms the state-of-the-art methods.