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

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

Anshika Choudhary, Anuja Arora
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 24
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781683180647|DOI: 10.4018/IJACI.309407
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MLA

Choudhary, Anshika, and Anuja Arora. "Continuous Attention Mechanism Embedded (CAME) Bi-Directional Long Short-Term Memory Model for Fake News Detection." IJACI vol.13, no.1 2022: pp.1-24. http://doi.org/10.4018/IJACI.309407

APA

Choudhary, A. & Arora, A. (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), 13(1), 1-24. http://doi.org/10.4018/IJACI.309407

Chicago

Choudhary, Anshika, and Anuja Arora. "Continuous Attention Mechanism Embedded (CAME) Bi-Directional Long Short-Term Memory Model for Fake News Detection," International Journal of Ambient Computing and Intelligence (IJACI) 13, no.1: 1-24. http://doi.org/10.4018/IJACI.309407

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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.

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