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：互联网的广泛接入和手持设备的普及赋予了社交媒体类似于传统报纸的影响力。人们通过社交媒体获取廉价信息，且几秒内即可触达。然而，这种便利性也伴随着风险：任何用户均可自由发布任何内容，且无论其真实性如何，该内容都可能在网络上长期存在。因此，识别不实信息（即假新闻）的需求日益凸显。本文提出了一种端到端解决方案，能够准确检测假新闻并实时免疫传播假新闻的网络节点。为检测假新闻，我们设计了两种新型堆叠式深度学习架构，融合了卷积层和双向LSTM层。为抑制假新闻的传播，我们提出了一种实时网络感知策略：（1）为目标检测节点构建最小代价加权有向生成树；（2）通过一种新型排序函数对树中节点进行危害性评分并实施免疫。我们在五个真实数据集上验证了该方案的有效性。