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Continuous Attention Mechanism Embedded (CAME) Bi-Directional Long Short-Term Memory Model for Fake News Detection

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  • Anshika Choudhary

    (Jaypee Institute of Information Technology, Noida, India)

  • Anuja Arora

    (Jaypee Institute of Information Technology, Noida, India)

Abstract

The credible analysis of news on social media due to the fact of spreading unnecessary restlessness and reluctance in the community is a need. Numerous individual or social media marketing entities radiate inauthentic news through online social media. Henceforth, delineating these activities on social media and the apparent identification of delusive content is a challenging task. This work projected a continuous attention-driven memory-based deep learning model to predict the credibility of an article. To exhibit the importance of continuous attention, research work is presented in accretive exaggeration mode. Initially, long short-term memory (LSTM)-based deep learning model has been applied, which is extended by merging the concept of bidirectional LSTM for fake news identification. This research work proposed a continuous attention mechanism embedded (CAME)-bidirectional LSTM model for predicting the nature of news. Result shows the proposed CAME model outperforms the performance as compared to LSTM and the bidirectional LSTM model.

Suggested Citation

  • Anshika Choudhary & Anuja Arora, 2022. "Continuous Attention Mechanism Embedded (CAME) Bi-Directional Long Short-Term Memory Model for Fake News Detection," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 13(1), pages 1-24, January.
    • Handle: RePEc:igg:jaci00:v:13:y:2022:i:1:p:1-24
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    Cited by:

    1. Choudhary, Anshika & Arora, Anuja, 2024. "Assessment of bidirectional transformer encoder model and attention based bidirectional LSTM language models for fake news detection," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).

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