The fast spreading of fake news stories on social media can cause inestimable social harm. Developing effective methods to detect them early is of paramount importance. A major challenge of fake news early detection is fully utilizing the limited data observed at the early stage of news propagation and then learning useful patterns from it for identifying fake news. In this article, we propose a novel deep neural network to detect fake news early. It has three novel components: (1) a status-sensitive crowd response feature extractor that extracts both text features and user features from combinations of users’ text response and their corresponding user profiles, (2) a position-aware attention mechanism that highlights important user responses at specific ranking positions, and (3) a multi-region mean-pooling mechanism to perform feature aggregation based on multiple window sizes. Experimental results on two real-world datasets demonstrate that our proposed model can detect fake news with greater than 90% accuracy within 5 minutes after it starts to spread and before it is retweeted 50 times, which is significantly faster than state-of-the-art baselines. Most importantly, our approach requires only 10% labeled fake news samples to achieve this effectiveness under PU-Learning settings.

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假新闻故事在社交媒体上的快速传播可能会造成不可估量的社会危害。开发有效的方法以及早发现它们至关重要。假新闻早期检测的一个主要挑战是充分利用在新闻传播早期观察到的有限数据，然后从中学习有用的模式来识别假新闻。在本文中，我们提出了一种新颖的深度神经网络来及早检测假新闻。它具有三个新颖的组件：（1）状态敏感的人群响应特征提取器，从用户的文本响应及其相应的用户配置文件的组合中提取文本特征和用户特征，（2）位置感知注意机制，突出重要的特定排名位置的用户反应，(3) 多区域均值池化机制，基于多个窗口大小执行特征聚合。在两个真实世界数据集上的实验结果表明，我们提出的模型可以在假新闻开始传播后的 5 分钟内和转发 50 次之前以超过 90% 的准确率检测到假新闻，这明显快于 state-of-the-艺术基线。最重要的是，我们的方法只需要 10% 的标记假新闻样本即可在 PU-Learning 设置下实现这一效果。