Hoax Identification On Social Media Using Recurrent Neural Network (RNN) And Long Short-term Memory (LSTM) Methods

Abstract

Technological developments support the rapid spread of hoaxes on social media. Information dissemination needs to be identified as hoaxes to prove that not all information can be received immediately. The speed at which news spreads through social media can be used as an obstacle to the identification of hoaxes. This can be overcome by taking direct data and then identifying it using the Recurrent Neural Network (RNN) method and Long Short-Term Memory (LSTM). The identification results show that the RNN classification method is a classification method with a greater increase in accuracy than the LSTM method, which is equal to 98.32% and for the LSTM the final accuracy value obtained is 95.63%.

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