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Enhancing the Predictive Performance of Credibility-Based Fake News Detection Using Ensemble Learning

Author

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  • Amit Neil Ramkissoon

    (The University of the West Indies at St Augustine)

  • Wayne Goodridge

    (The University of the West Indies at St Augustine)

Abstract

Fake news detection continues to be a major problem that affects our society today. Fake news can be classified using a variety of methods. Predicting and detecting fake news has proven to be challenging even for machine learning algorithms. This research employs Legitimacy, a unique ensemble machine learning model to accomplish the task of Credibility-Based Fake News Detection. The Legitimacy ensemble combines the learning potential of a Two-Class Boosted Decision Tree and a Two-Class Neural Network. The ensemble technique follows a pseudo-mixture-of-experts methodology. For the gating model, an instance of Two-Class Logistic Regression is implemented. This study validates Legitimacy using a standard dataset with features relating to the credibility of news publishers to predict fake news. These features are analysed using the ensemble algorithm. The results of these experiments are examined using four evaluation methodologies. The analysis of the results reveals positive performance with the use of the ensemble ML method with an accuracy of 96.9%. This ensemble’s performance is compared with the performance of the two base machine learning models of the ensemble. The performance of the ensemble surpasses that of the two base models. The performance of Legitimacy is also analysed as the size of the dataset increases to demonstrate its scalability. Hence, based on our selected dataset, the Legitimacy ensemble model has proven to be most appropriate for Credibility-Based Fake News Detection.

Suggested Citation

  • Amit Neil Ramkissoon & Wayne Goodridge, 2022. "Enhancing the Predictive Performance of Credibility-Based Fake News Detection Using Ensemble Learning," The Review of Socionetwork Strategies, Springer, vol. 16(2), pages 259-289, October.
    • Handle: RePEc:spr:trosos:v:16:y:2022:i:2:d:10.1007_s12626-022-00127-7
    DOI: 10.1007/s12626-022-00127-7
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    References listed on IDEAS

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    1. Iftikhar Ahmad & Muhammad Yousaf & Suhail Yousaf & Muhammad Ovais Ahmad, 2020. "Fake News Detection Using Machine Learning Ensemble Methods," Complexity, Hindawi, vol. 2020, pages 1-11, October.
    2. Amit Neil Ramkissoon & Shareeda Mohammed & Wayne Goodridge, 2021. "Determining an Optimal Data Classification Model for Credibility-Based Fake News Detection," The Review of Socionetwork Strategies, Springer, vol. 15(2), pages 347-380, November.
    3. Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
    4. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
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