Rumour Veracity Estimation with Deep Learning for Twitter

  • Jyoti Prakash Singh
  • Nripendra P. RanaEmail author
  • Yogesh K. Dwivedi
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 558)


Twitter has become a fertile ground for rumours as information can propagate to too many people in very short time. Rumours can create panic in public and hence timely detection and blocking of rumour information is urgently required. We proposed and compare machine learning classifiers with a deep learning model using Recurrent Neural Networks for classification of tweets into rumour and non-rumour classes. A total thirteen features based on tweet text and user characteristics were given as input to machine learning classifiers. Deep learning model was trained and tested with textual features and five user characteristic features. The findings indicate that our models perform much better than machine learning based models.


Rumour veracity Deep learning Twitter Neural network Machine learning 


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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Jyoti Prakash Singh
    • 1
  • Nripendra P. Rana
    • 2
    Email author
  • Yogesh K. Dwivedi
    • 2
  1. 1.National Institute of Technology PatnaBiharIndia
  2. 2.School of ManagementSwansea University Bay CampusSwanseaUK

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