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.

翻译：在线社交网络因信息传播成本低廉已成为虚假新闻的温床。现有方法虽在新闻内容与传播结构方面进行了诸多尝试，但虚假新闻检测仍面临两大挑战：一是如何挖掘独特的核心特征与演化模式，二是如何解决小样本问题以构建高性能模型。与充分利用传播拓扑结构的流行方法不同，本文从语义、情感与数据增强三个维度提出一种新型虚假新闻检测框架。该框架挖掘新闻传播过程中参与者的情感演化模式，并设计语义与情感的双深度交互通道网络，结合评论信息获得更全面且细粒度的新闻表征。同时，框架引入基于置信度的数据增强模块，以获取更高质量的有标注数据，进一步提升分类模型性能。实验表明，所提方法优于当前最先进方法。