With the widespread use of online social media, we have witnessed that fake news causes enormous distress and inconvenience to people's social life. Although previous studies have proposed rich machine learning methods for identifying fake news in social media, the task of detecting fake news in emerging news events/domains remains a challenging problem due to the wide range of news topics on social media as well as the evolution and variation of fake news contents in the web. In this study, we propose an approach which we term “domain-adversarial and graph-attention neural network” (DAGA-NN) model to address the challenge. Its main advantage is that, in a text environment with multiple events/domains, only partial domain sample data are needed to train the model to achieve accurate cross-domain fake news detection in those domains with few (or even no) samples, which makes up for the limitations of traditional machine learning in fake news detection tasks due to news content evolution or cross-domain identification (where there is no sample data). Extensive experiments were conducted on two multimedia datasets of Twitter and Weibo, and the results showed that the proposed model was very effective in detecting fake news across events/domains.

随着网络社交媒体的广泛使用，我们目睹了假新闻给人们的社交生活带来了巨大的困扰和不便。尽管之前的研究提出了丰富的机器学习方法来识别社交媒体中的假新闻，但由于社交媒体上的新闻主题范围广泛以及不断发展和演变，在新兴新闻事件/领域中检测假新闻的任务仍然是一个具有挑战性的问题。网络中虚假新闻内容的变化。在这项研究中，我们提出了一种称为“域对抗和图形注意神经网络”（DAGA-NN）模型的方法来应对挑战。它的主要优点是，在具有多个事件/域的文本环境中，只需要部分域样本数据来训练模型，在那些样本很少（甚至没有）的域中实现准确的跨域假新闻检测，弥补了传统机器学习在假新闻检测任务中因新闻而产生的局限性内容演变或跨域识别（没有样本数据）。在 Twitter 和微博的两个多媒体数据集上进行了广泛的实验，结果表明所提出的模型在检测跨事件/域的假新闻方面非常有效。