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

TL;DR

This paper introduces a two-path deep semi-supervised learning framework using CNNs for timely fake news detection on social media, effectively leveraging limited labeled data and abundant unlabeled data.

Contribution

It proposes a novel joint CNN-based semi-supervised learning framework with shared feature extraction for fake news detection.

Findings

Effective detection with minimal labeled data

Improved accuracy on LIAR and PHEME datasets

Joint optimization enhances learning performance

Abstract

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.