With the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world. With deceptive words, people can get infected by the fake news very easily and will share them without any fact-checking. For instance, during the 2016 US president election, various kinds of fake news about the candidates widely spread through both official news media and the online social networks. These fake news is usually released to either smear the opponents or support the candidate on their side. The erroneous information in the fake news is usually written to motivate the voters' irrational emotion and enthusiasm. Such kinds of fake news sometimes can bring about devastating effects, and an important goal in improving the credibility of online social networks is to identify the fake news timely. In this paper, we propose to study the fake news detection problem. Automatic fake news identification is extremely hard, since pure model based fact-checking for news is still an open problem, and few existing models can be applied to solve the problem. With a thorough investigation of a fake news data, lots of useful explicit features are identified from both the text words and images used in the fake news. Besides the explicit features, there also exist some hidden patterns in the words and images used in fake news, which can be captured with a set of latent features extracted via the multiple convolutional layers in our model. A model named as TI-CNN (Text and Image information based Convolutinal Neural Network) is proposed in this paper. By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously. Extensive experiments carried on the real-world fake news datasets have demonstrate the effectiveness of TI-CNN.
翻译:随着社交网络的发展,各种商业和政治目的的假新闻出现了数量众多且在网络世界中广泛流传。用欺骗性的话语,人们可以很容易地很容易地受到假新闻的感染,并且可以在不进行任何事实检查的情况下分享。例如,在2016年美国总统选举期间,通过官方新闻媒体和在线社交网络广泛通过官方新闻媒体和在线社交网络传播有关候选人的各种假新闻。这些假新闻通常发布,不是污蔑反对派,就是支持候选人的。假新闻中的错误信息通常会大量出现,在网上世界中广泛出现。这类假新闻的错误信息通常写成,以激励选民的不理性情绪和热情。这类假新闻有时会带来破坏性的效果,而提高在线社交网络信誉的一个重要目标是识别假新闻。在这份报纸上,我们提议研究假新闻探测的问题。由于纯粹基于事实检查的模型仍然是一个公开的问题,因此很少有现成的模型可用于解决问题。通过对假新闻数据进行彻底调查后,在假的纸面纸上可以找到许多有用的明确信息,从我们假CN使用的文字和图像中可以找到一些有用的明确的信息。在模拟新闻中使用的文本和图像中,在模拟的网络上,在模拟新闻中可以看到一个纸面新闻中,一个隐藏的图像中,一个隐藏的图像中,一个秘密的图像,用一个秘密的图文中可以展示的图中可以展示的图中,用一个是的图式的图文的图文的图中,用的图文的图中可以展示的图文的图文,用的图中可以展示的图文的图中可以展示的图文的图文的图文的图文的图文的图中可以显示。一个。一个,通过一个,用的图的图的图的图的图的图文的图的图的图,通过一个是的图文的图的图的图的图的图的图,用的图的图,用的图,通过在模型的图和图,用的图的图的图的图的图的图的图的图中可以在模型的图的图中,在一个是的图和图的图的图的图中,在模型的图和图和图和图和图和图和图的图的图的图的图的图的图的图的图的图的图的图的图的图