社交网络已经成为人们日常生活中获取和分享信息的主要渠道,同时也为虚假新闻的传播提供了捷径.如今,针对网络虚假新闻的检测问题受到学术界的广泛关注,但目前的检测方法缺乏基于新闻多个视角的深度探索或忽视了新闻中不同信息传播方向不同的问题,有待改进.文章提出一种基于新闻内容、用户信息和新闻传播3种视角的多视图表征和检测的模型MVRFD(Multi-View Representations for Fake News Detection),为虚假新闻检测任务提供更全面的视角.首先,利用协同注意力机制表征新闻内容中的多模态信息,使用具有不同方向的图神经网络聚合新闻传播过程中的用户信息和观点信息;然后,利用双协同注意力机制实现多个视角间的信息交互;最后,将新闻内容特征和新闻上下文特征进行融合.在公开数据集上的实验结果表明,文章所提出的模型实现了 96.7％的准确率和96.8％的F1值,优于主流的文本处理模型以及基于单视角的检测模型.
Multi-View Representations for Fake News Detection
Social networks have become a major channel for people to access and share information in their daily lives,while also providing shortcuts for the spread of fake news.Nowadays,the detection of online fake news has been widely concerned and studied by the academic community,but the current methods lack in-depth exploration based on multiple perspectives of news or ignore the different directions of different information in news.In order to provide a more comprehensive perspective for fake news detection task,this paper proposed a multi-view representations for fake news detection(MVRFD)model based on three perspectives:news content,user information and news propagation.Firstly,the co-attention mechanism was used to represent the multimodal information in news content,and the graph neural network with different directions was used to aggregate user information and views in the process of news transmission.Then the dual-co-attention mechanism was used to realize the information interaction between multiple perspectives.Finally,the features of news content and the news context were integrated.Experiments on the publicly available dataset show that the proposed model achieves 96.7％accuracy and 96.8％F1 score,which are better than the mainstream text processing models and single-view-based detection models.
NETL
NSTL
万方数据
