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Research Article

Integrating Metaheuristics and Two-Tiered Classification for Enhanced Fake News Detection with Feature Optimization

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  • @ARTICLE{10.4108/eetsis.5069,
        author={Poonam Narang and Ajay Vikram Singh and Himanshu Monga},
        title={Integrating Metaheuristics and Two-Tiered Classification for Enhanced Fake News Detection with Feature Optimization},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={6},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={4},
        keywords={Recurrent Neural Networks (RNN), Red deer optimization, African vulture Optimization, RBM, Fake News, Bi-LSTM},
        doi={10.4108/eetsis.5069}
    }
    
  • Poonam Narang
    Ajay Vikram Singh
    Himanshu Monga
    Year: 2024
    Integrating Metaheuristics and Two-Tiered Classification for Enhanced Fake News Detection with Feature Optimization
    SIS
    EAI
    DOI: 10.4108/eetsis.5069
Poonam Narang1,*, Ajay Vikram Singh1, Himanshu Monga2
  • 1: Amity University
  • 2: Government Hydro Engineering College
*Contact email: hipoonam@gmail.com

Abstract

INTRODUCTION: The challenge of distributing false information continues despite the significant impact of social media on opinions. The suggested framework, which is a metaheuristic method, is presented in this research to detect bogus news. Employing a hybrid metaheuristic RDAVA methodology coupled with Bi-LSTM, the method leverages African Vulture Optimizer and Red Deer Optimizer. OBJECTIVES: The objective of this study is to assess the effectiveness of the suggested model in identifying false material on social media by employing social network analysis tools to combat disinformation. METHODS: Employing the data sets from BuzzFeed, FakeNewsNet, and ISOT, the suggested model is implemented on the MATLAB Platform and acquires high accuracy rates of 97% on FakeNewsNet and 98% on BuzzFeed and ISOT. A comparative study with current models demonstrates its superiority. RESULTS: Outperforming previous models with 98% and 97% accuracy on BuzzFeed/ISOT and FakeNewsNet, respectively, the suggested model shows remarkable performance. CONCLUSION: The proposed strategy shows promise in addressing the problem of false information on social media in the modern day by effectively countering fake news. Its incorporation of social network analysis methods and metaheuristic methodologies makes it a powerful instrument for identifying false news.

Keywords
Recurrent Neural Networks (RNN), Red deer optimization, African vulture Optimization, RBM, Fake News, Bi-LSTM
Received
2024-02-08
Accepted
2024-04-02
Published
2024-04-03
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.5069

Copyright © 2024 P. Narang et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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