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Machine Learning-Based Rumor Controlling

In: Handbook for Management of Threats

Author

Listed:
  • Ke Su

    (University of Texas at Dallas)

  • Priyanshi Garg

    (University of Texas at Dallas)

  • Weili Wu

    (University of Texas at Dallas)

  • Ding-Zhu Du

    (University of Texas at Dallas)

Abstract

In the management of business or political battle, the rumor or disinformation is an important issue to be dealt with. Especially, with the rise of Web 2.0, online social networks (OSN) have been an important way for people to access information. OSN enables rapid dissemination of information but lacks fact-checking mechanisms, which leads to the widespread rumor problem. Many researchers have made great efforts to control rumors with machine learning technology. In this chapter, we provide a comprehensive review for existing efforts on how to overcome rumor detection, rumor source detection, and rumor prevention. From this review, we intend to find new research problems and valuable research potentials.

Suggested Citation

  • Ke Su & Priyanshi Garg & Weili Wu & Ding-Zhu Du, 2023. "Machine Learning-Based Rumor Controlling," Springer Optimization and Its Applications, in: Konstantinos P. Balomenos & Antonios Fytopoulos & Panos M. Pardalos (ed.), Handbook for Management of Threats, pages 341-370, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-39542-0_17
    DOI: 10.1007/978-3-031-39542-0_17
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