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Rumour Veracity Estimation with Deep Learning for Twitter

Abstract : 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.
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https://hal.inria.fr/hal-02294696
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Submitted on : Monday, September 23, 2019 - 4:07:57 PM
Last modification on : Wednesday, June 9, 2021 - 3:26:02 PM
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Jyoti Singh, Nripendra Rana, Yogesh 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⟩

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