2019/06/10 by Xishuang Dong, Uboho Victor, Dong, Xishuang +5
Computer Science · Social Sciences · #Advanced Malware Detection Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Misinformation and Its Impacts #Spam and Phishing Detection
paper · pdf · doi:10.48550/arxiv.1906.05659
openalex publication_date 2019/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
News in social media such as Twitter has been generated in high volume and speed. However, very few of them can be labeled (as fake or true news) in a short time. In order to achieve timely detection of fake news in social media, a novel deep two-path semi-supervised learning model is proposed, where one path is for supervised learning and the other is for unsupervised learning. These two paths implemented with convolutional neural networks are jointly optimized to enhance detection performance. In addition, we build a shared convolutional neural networks between these two paths to share the low level features. Experimental results using Twitter datasets show that the proposed model can recognize fake news effectively with very few labeled data.