Abstract

  Fake news propagation is identified as the most alarming problem in India. The use of social media has an immense effect on the business, culture and society; it is one of the sources/spread of fake news. We propose a novel, robust framework with Recurrent Neural Network Augmented with Cellular Automata (RNN-CA), which was trained and tested with standard datasets taken from Kaggle and other prominent news websites to identify the fake messages. The proposed approach meticulously identifies the set of features needed to predict the fake news and messages with an average accuracy of 89.76%. We have used various parameters like specificity, sensitivity and average accuracy for validating our classifier. RNN-CA performance is compared with the existing literature and it was found promising.

Keywords: Fake News; Recurrent Neural Network (RNN); Cellular Automata (CA)

References

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Citation

Citation: Pokkuluri Kiran Sree and SSSN Usha Devi N. "Fake News Identification Using Recurrent Neural Network Augmented with Cellular Automata". Acta Scientific Computer Sciences 2.11 (2020): 39-41.

Copyright

Copyright: © 2020 Pokkuluri Kiran Sree and SSSN Usha Devi N. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.