Fake News Identification on Twitter with Hybrid CNN and RNN Models

TL;DR

This paper presents a hybrid CNN and RNN deep learning framework that detects and classifies fake news on Twitter with 82% accuracy, identifying relevant features without prior domain knowledge.

Contribution

It introduces a novel hybrid deep learning model combining CNN and RNN for fake news detection on social media.

Findings

Achieves 82% accuracy in fake news classification

Identifies relevant features without domain-specific knowledge

Demonstrates effectiveness of hybrid deep learning models

Abstract

The problem associated with the propagation of fake news continues to grow at an alarming scale. This trend has generated much interest from politics to academia and industry alike. We propose a framework that detects and classifies fake news messages from Twitter posts using hybrid of convolutional neural networks and long-short term recurrent neural network models. The proposed work using this deep learning approach achieves 82% accuracy. Our approach intuitively identifies relevant features associated with fake news stories without previous knowledge of the domain.