论文标题
两路深度半监督的学习及时伪造新闻检测
Two-path Deep Semi-supervised Learning for Timely Fake News Detection
论文作者
论文摘要
在Twitter等社交媒体中的新闻已经以大量和速度产生。但是，很少有专业人士几乎实时将其中很少有人标记为（假或真实新闻）。为了在社交媒体中及时检测虚假新闻，提出了一个新颖的两路深度半监督学习的框架，其中一条路是用于监督学习的道路，而另一种是无监督的学习。监督的学习路径了解有限的标记数据，而无监督的学习路径能够了解大量未标记的数据。此外，通过卷积神经网络（CNN）实施的这两条路径被共同优化以完成半监督学习。此外，我们构建了共享的CNN，以在标记的数据和未标记的数据上提取低级功能，以将其馈入这两个路径。为了验证该框架，我们实现了一个基于CNN的单词半监督学习模型，并在两个数据集上进行测试，即骗子和Pheme。实验结果表明，建立在提议的框架上的模型可以通过很少的标记数据有效地识别假新闻。
News in social media such as Twitter has been generated in high volume and speed. However, very few of them are labeled (as fake or true news) by professionals in near real time. In order to achieve timely detection of fake news in social media, a novel framework of two-path deep semi-supervised learning is proposed where one path is for supervised learning and the other is for unsupervised learning. The supervised learning path learns on the limited amount of labeled data while the unsupervised learning path is able to learn on a huge amount of unlabeled data. Furthermore, these two paths implemented with convolutional neural networks (CNN) are jointly optimized to complete semi-supervised learning. In addition, we build a shared CNN to extract the low level features on both labeled data and unlabeled data to feed them into these two paths. To verify this framework, we implement a Word CNN based semi-supervised learning model and test it on two datasets, namely, LIAR and PHEME. Experimental results demonstrate that the model built on the proposed framework can recognize fake news effectively with very few labeled data.
