Machine Learning-Based Rumor Controlling

Ke Su (), Priyanshi Garg (), Weili Wu () and Ding-Zhu Du ()
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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

A chapter in Handbook for Management of Threats, 2023, pp 341-370 from Springer

Abstract: 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.

Keywords: Social networks; Rumor blocking; Machine learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-39542-0_17

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