Rumour Veracity Estimation with Deep Learning for Twitter - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2019

Rumour Veracity Estimation with Deep Learning for Twitter

Jyoti Prakash Singh
  • Function : Author
  • PersonId : 1024444
Nripendra P. Rana
  • Function : Author
  • PersonId : 999539
Yogesh K. Dwivedi
  • Function : Author
  • PersonId : 999540


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.
Fichier principal
Vignette du fichier
484827_1_En_24_Chapter.pdf (475.06 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02294696 , version 1 (23-09-2019)





Jyoti Prakash Singh, Nripendra P. Rana, Yogesh K. Dwivedi. Rumour Veracity Estimation with Deep Learning for Twitter. International Working Conference on Transfer and Diffusion of IT (TDIT), Jun 2019, Accra, Ghana. pp.351-363, ⟨10.1007/978-3-030-20671-0_24⟩. ⟨hal-02294696⟩
117 View
48 Download



Gmail Facebook X LinkedIn More