Year: 2019
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Institutions: Texas A&M University System, Prairie View A&M University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2952632192