Fake News Identification on Twitter with Hybrid CNN and RNN Models

By Oluwaseun Ajao, Deepayan Bhowmik, Shahrzad Zargari. International Conference on Social Media & Society, 2018

310 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/063f217940b3051dfbff80f3157e61cec8fdb8e9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Francesco Marcelloni
2. Alessandro Bondielli

In Information Systems

1. Francesco Marcelloni
2. Alessandro Bondielli

In Sociology and Political Science

1. Francesco Marcelloni
2. Alessandro Bondielli

Citations in the corpus

• A survey on fake news and rumour detection techniques (2019, methodology): “[2] has performed some experiments using LSTM and hybrid LSTM-CNN architectures.”