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Social Network Analysis for Disinformation Detection

In: Machine Learning for Data Science Handbook

Author

Listed:
  • Aviad Elyashar

    (Ben-Gurion University of the Negev, Department of Software and Information Systems Engineering)

  • Maor Reuben

    (Ben-Gurion University of the Negev, Department of Software and Information Systems Engineering)

  • Asaf Shabtai

    (Ben-Gurion University of the Negev, Department of Software and Information Systems Engineering)

  • Rami Puzis

    (Ben-Gurion University of the Negev, Department of Software and Information Systems Engineering)

Abstract

Fake news is a long-lasting problem which has drawn significant attention in recent years. There is a growing need for tools and methods to control the spread of misinformation through online social media. Machine learning methods have been utilized to pinpoint linguistic patterns, influential accounts, or spreading dynamics associated with misinformation. In this paper, we present an automated process for training fake news classifiers based on multiple families of features extracted from social media. In addition to the high accuracy of the trained machine learning classifiers, our results show that online social media users are aware of deceptive content and can often provide reliable feedback for the detection of fake news.

Suggested Citation

  • Aviad Elyashar & Maor Reuben & Asaf Shabtai & Rami Puzis, 2023. "Social Network Analysis for Disinformation Detection," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 681-701, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_30
    DOI: 10.1007/978-3-031-24628-9_30
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