IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v11y2020i2d10.1007_s13198-020-01016-4.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-020-01016-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13198-020-01016-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muhammad Mudassar Yamin & Mohib Ullah & Habib Ullah & Basel Katt & Mohammad Hijji & Khan Muhammad, 2022. "Mapping Tools for Open Source Intelligence with Cyber Kill Chain for Adversarial Aware Security," Mathematics, MDPI, vol. 10(12), pages 1-25, June.
    2. Andrea Stevens Karnyoto & Chengjie Sun & Bingquan Liu & Xiaolong Wang, 2022. "TB-BCG: Topic-Based BART Counterfeit Generator for Fake News Detection," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    3. Balasubramanian Palani & Sivasankar Elango, 2023. "CTrL-FND: content-based transfer learning approach for fake news detection 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. 14(3), pages 903-918, June.
    4. Kim, Jong Min & Park, Keeyeon Ki-cheon & Mariani, Marcello & Wamba, Samuel Fosso, 2024. "Investigating reviewers' intentions to post fake vs. authentic reviews based on behavioral linguistic features," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    5. Li, Ziqi & Shi, Chaoyi & Zhang, Qi & Chu, Tianguang, 2024. "Inferring the source of diffusion in networks under weak observation condition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    6. Mohammad Zubair Khan & Omar Hussain Alhazmi, 0. "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. 0, pages 1-9.
    7. Cano-Marin, Enrique & Mora-Cantallops, Marçal & Sanchez-Alonso, Salvador, 2023. "The power of big data analytics over fake news: A scientometric review of Twitter as a predictive system in healthcare," Technological Forecasting and Social Change, Elsevier, vol. 190(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:11:y:2020:i:2:d:10.1007_s13198-020-01016-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.