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News in social media such as Twitter has been generated in high volume andspeed. However, very few of them are labeled (as fake or true news) byprofessionals in near real time. In order to achieve timely detection of fakenews in social media, a novel framework of two-path deep semi-supervisedlearning is proposed where one path is for supervised learning and the other isfor unsupervised learning. The supervised learning path learns on the limitedamount of labeled data while the unsupervised learning path is able to learn ona huge amount of unlabeled data. Furthermore, these two paths implemented withconvolutional neural networks (CNN) are jointly optimized to completesemi-supervised learning. In addition, we build a shared CNN to extract the lowlevel features on both labeled data and unlabeled data to feed them into thesetwo paths. To verify this framework, we implement a Word CNN basedsemi-supervised learning model and test it on two datasets, namely, LIAR andPHEME. Experimental results demonstrate that the model built on the proposedframework can recognize fake news effectively with very few labeled data.

Two-path Deep Semi-supervised Learning for Timely Fake News Detection