The widespread availability of internet access and handheld devices confers to social media a power similar to the one newspapers used to have. People seek affordable information on social media and can reach it within seconds. Yet this convenience comes with dangers; any user may freely post whatever they please and the content can stay online for a long period, regardless of its truthfulness. A need to detect untruthful information, also known as fake news, arises. In this paper, we present an end-to-end solution that accurately detects fake news and immunizes network nodes that spread them in real-time. To detect fake news, we propose two new stack deep learning architectures that utilize convolutional and bidirectional LSTM layers. To mitigate the spread of fake news, we propose a real-time network-aware strategy that (1) constructs a minimum-cost weighted directed spanning tree for a detected node, and (2) immunizes nodes in that tree by scoring their harmfulness using a novel ranking function. We demonstrate the effectiveness of our solution on five real-world datasets.
翻译:互联网接入和手持装置的普及给社交媒体带来了一种类似于过去一家报纸所拥有的权力。人们在社交媒体上寻找负担得起的信息,可以在几秒钟内达到。然而,这种方便带来危险;任何用户都可以自由地张贴他们想要的东西,内容可以长期上网,不管其真实性如何。需要检测不真实的信息,也称为假新闻。在本文中,我们提出了一个端到端的解决方案,准确检测假新闻,并给实时传播这些新闻的网络节点注射免疫。为了检测假新闻,我们提出了两套新的深层学习结构,利用共和双向LSTM层。为了减少假新闻的传播,我们提出了一个实时网络认知战略,即(1) 为检测到的节点设计一个最低成本加权的直线树,(2) 使用新的分级功能对树的坏处进行评分。我们展示了五个真实世界数据集的解决方案的有效性。