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Intelligent Fake News Detection Model: An Attention-Driven Deep Learning with Improved Chimp Optimization Using YouTube Comments

International Journal of Electronics and Communication Engineering
© 2025 by SSRG - IJECE Journal
Volume 12 Issue 6
Year of Publication : 2025
Authors : R. Bharathi, R. Rajarajan
10.14445/23488549/IJECE-V12I6P112
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How to Cite?

R. Bharathi, R. Rajarajan, "Intelligent Fake News Detection Model: An Attention-Driven Deep Learning with Improved Chimp Optimization Using YouTube Comments," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 6, pp. 147-157, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I6P112

Abstract:

In recent years, much information has been exchanged over the Internet, especially on social media platforms, which is developing rapidly. YouTube is the foremost social media network on which to share videos and comments. Lately, the rapid expansion of online platforms has resulted in a substantial increase in false information. Fake news has become pervasive, frustrating, and distracting across various sites. It has excellent effects on either society or individuals. However, developing an effective recognition method is crucial to categorize fake news, as it has become a crucial issue threatening the reliability of social networks. Consequently, Machine Learning (ML) and Deep Learning (DL) techniques have more precisely progressed in perceiving fake news. This study proposes an Attention-Driven Fake News Detection with Improved Chimp Optimization using YouTube Comments (ADFND-ICOYC) model. Initially, the text pre-processing stage cleans and transforms raw text into a structured format. Furthermore, the word2vec method is utilized for extraction. Moreover, the proposed ADFND-ICOYC model employs The Bidirectional Long Short-Term Memory and Attention Mechanism (BiLSTM-AM) method for classification. Finally, the Improved Chimp Optimization Algorithm (ICOA) optimally alters the hyperparameter value of the BiLSTM-AM method and results in higher classification performance. The efficacy of the ADFND-ICOYC method is examined under the Fake News Detection dataset. The performance analysis of the ADFND-ICOYC method demonstrated a superior accuracy value of 96.88% over existing models.

Keywords:

Fake News Detection Improved Chimp Optimization Algorithm, YouTube comments, Text pre-processing, Feature extraction.

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