Fake news is fabricated information that is presented as genuine, with intention to deceive the reader. Recently, the magnitude of people relying on social media for news consumption has increased significantly. Owing to this rapid increase, the adverse effects of misinformation affect a wider audience. On account of the increased vulnerability of people to such deceptive fake news, a reliable technique to detect misinformation at its early stages is imperative. Hence, the authors propose a novel graph-based framework SOcial graph with Multi-head attention and Publisher information and news Statistics Network (SOMPS-Net) comprising of two components - Social Interaction Graph (SIG) and Publisher and News Statistics (PNS). The posited model is experimented on the HealthStory dataset and generalizes across diverse medical topics including Cancer, Alzheimer's, Obstetrics, and Nutrition. SOMPS-Net significantly outperformed other state-of-the-art graph-based models experimented on HealthStory by 17.1%. Further, experiments on early detection demonstrated that SOMPS-Net predicted fake news articles with 79% certainty within just 8 hours of its broadcast. Thus the contributions of this work lay down the foundation for capturing fake health news across multiple medical topics at its early stages.
翻译：假消息是编造出来的真实信息,目的是欺骗读者。最近,依靠社交媒体进行新闻消费的人数大幅增加。由于这一快速增长,错误信息的不利影响影响到更多的受众。由于人们越来越容易受到这种欺骗性假新闻的影响,因此,必须采用可靠的方法在早期阶段发现错误信息。因此,作者提出了一个新的基于图表的框架Social图,由多头目的关注和出版商信息和新闻统计网(SOMPS-Net)组成,由两个组成部分组成:社会互动图(SIG)和出版商和新闻统计(PNS)。 假设模型在卫生故事数据集上进行实验,并概括了各种医学主题,包括癌症、老年痴呆症、产科和营养。SOMPS-Net大大优于17.1%的基于健康故事的基于图表的其他实验。此外,早期检测实验表明,SOMPS-Net在广播的8小时之内就以79%的确定度预测了假新闻文章。因此,其早期工作的贡献在多个医学阶段为获取了假新闻的基础。
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