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

TL;DR

This paper introduces a deep two-path semi-supervised learning model utilizing CNNs for effective fake news detection on social media with limited labeled data.

Contribution

It proposes a novel dual-path CNN framework combining supervised and unsupervised learning for timely fake news detection.

Findings

Effective recognition of fake news with minimal labeled data

Joint optimization of dual CNN paths improves detection accuracy

Shared CNN layers enhance feature learning efficiency

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.