论文标题
为假新闻检测建模社会环境：基于图神经网络的方法
Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach
论文作者
论文摘要
假新闻的检测对于确保信息的真实性并保持新闻生态系统的可靠性至关重要。最近，由于最近的社交媒体和伪造的内容生成技术（例如Deep Fake）的泛滥，假新闻内容的增加了。假新闻检测的大多数现有方式都集中在基于内容的方法上。但是，这些技术中的大多数无法处理生成模型生产的超现实合成媒体。我们最近的研究发现，真实和虚假新闻的传播特征是可以区分的，无论其方式如何。在这方面，我们已经根据社会环境调查了辅助信息，以检测假新闻。本文通过基于混合图神经网络的方法分析了假新闻检测的社会背景。该混合模型基于将图形神经网络整合在新闻内容上的新闻和双向编码器表示的传播中，以了解文本功能。因此，这种提出的方法可以学习内容以及上下文特征，因此能够在Politifact上以F1得分为0.91和0.93的基线模型，在八卦数据集上分别为0.93
Detection of fake news is crucial to ensure the authenticity of information and maintain the news ecosystems reliability. Recently, there has been an increase in fake news content due to the recent proliferation of social media and fake content generation techniques such as Deep Fake. The majority of the existing modalities of fake news detection focus on content based approaches. However, most of these techniques fail to deal with ultra realistic synthesized media produced by generative models. Our recent studies find that the propagation characteristics of authentic and fake news are distinguishable, irrespective of their modalities. In this regard, we have investigated the auxiliary information based on social context to detect fake news. This paper has analyzed the social context of fake news detection with a hybrid graph neural network based approach. This hybrid model is based on integrating a graph neural network on the propagation of news and bi directional encoder representations from the transformers model on news content to learn the text features. Thus this proposed approach learns the content as well as the context features and hence able to outperform the baseline models with an f1 score of 0.91 on PolitiFact and 0.93 on the Gossipcop dataset, respectively
