e-space
Manchester Metropolitan University's Research Repository

    Fake News identification on Twitter with Hybrid CNN and RNN Models

    Ajao, Oluwaseun ORCID logoORCID: https://orcid.org/0000-0002-6606-6569, Bhowmik, Deepayan and Zargari, Shahrzad (2018) Fake News identification on Twitter with Hybrid CNN and RNN Models. In: SMSociety '18: International Conference on Social Media and Society, 18 July 2018 - 20 July 2018, Copenhagen, Denmark.

    [img]
    Preview
    Accepted Version
    Available under License In Copyright.

    Download (122kB) | Preview

    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.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    47Downloads
    6 month trend
    12Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record