Fake news identification on Twitter with hybrid CNN and RNN models

AJAO, Oluwaseun, BHOWMIK, Deepayan and ZARGARI, Shahrzad (2018). Fake news identification on Twitter with hybrid CNN and RNN models. In: SMSociety '18 Proceedings of the 9th International Conference on Social Media and Society. ACM International Conference Proceeding Series . ACM, 226-230.

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Official URL: https://dl.acm.org/citation.cfm?id=3217917
Link to published version:: https://doi.org/10.1145/3217804.3217917

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.

Item Type: Book Section
Additional Information: 9th International Conference on Social Media & Society Copenhagen, Denmark, July 18-20, 2018
Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
Identification Number: https://doi.org/10.1145/3217804.3217917
Related URLs:
Depositing User: Shahrzad Zargari
Date Deposited: 09 Aug 2018 08:53
Last Modified: 16 Nov 2018 14:11
URI: http://shura.shu.ac.uk/id/eprint/21868

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