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Study and analysis of unreliable news based on content acquired using ensemble learning (prevalence of fake news on social media)

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  • Mohammad Zubair Khan

    (Taibah University)

  • Omar Hussain Alhazmi

    (Taibah University)

Abstract

We explore the use of machine learning techniques to classify a news source for generating unreliable news. Since the advent of the Internet, unreliable news and hoaxes have deceived users. Social media and news outlets are spreading false information to increase the number of viewers or as a part of the psychological competition. In this paper, we present an ensemble classifier using a set of marked true and bogus news articles. Here, the authors develop a classification approach based on text using SVM, Random-Forest, Naïve Bayes, Decision Tree as a base learner in Bagging and AdaBoost. The purpose behind the work is to think of an answer that enable the user to classify and filter some of the false material. Accordingly, we show that the best performing classifiers were AdaBoost-LinearSVM and AdaBoost-Random Forest with 90.70% and 80.17% accuracy, respectively.

Suggested Citation

  • Mohammad Zubair Khan & Omar Hussain Alhazmi, 2020. "Study and analysis of unreliable news based on content acquired using ensemble learning (prevalence of fake news on social media)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 145-153, July.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:2:d:10.1007_s13198-020-01016-4
    DOI: 10.1007/s13198-020-01016-4
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    References listed on IDEAS

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    1. Ozbay, Feyza Altunbey & Alatas, Bilal, 2020. "Fake news detection within online social media using supervised artificial intelligence algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
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