A survey on rumor detection and prevention in social media using deep learning.

Autor: Pattanaik B; School of Computer Science and Engineering, XIM University, Bhubaneswar, Odisha 752050 India., Mandal S; School of Computer Science and Engineering, XIM University, Bhubaneswar, Odisha 752050 India., Tripathy RM; School of Computer Science and Engineering, XIM University, Bhubaneswar, Odisha 752050 India.
Jazyk: angličtina
Zdroj: Knowledge and information systems [Knowl Inf Syst] 2023 May 29, pp. 1-42. Date of Electronic Publication: 2023 May 29.
DOI: 10.1007/s10115-023-01902-w
Abstrakt: In the current digital era, massive amounts of unreliable, purposefully misleading material, such as texts and images, are being shared widely on various web platforms to deceive the reader. Most of us use social media sites to exchange or obtain information. This opens a lot of space for false information, like fake news, rumors, etc., to spread that could harm a society's social fabric, a person's reputation, or the legitimacy of a whole country. Therefore, preventing the transmission of such dangerous material across platforms is a digital priority. However, the main goal of this survey paper is to thoroughly examine several current state-of-the-art research works on rumor control (detection and prevention) that use deep learning-based techniques and to identify major distinctions between these research efforts. The comparison results are intended to identify research gaps and challenges for rumor detection, tracking, and combating. This survey of the literature makes a significant contribution by highlighting several cutting-edge deep learning-based models for rumor detection in social media and critically evaluating their effectiveness on recently available standard datasets. Furthermore, to have a thorough grasp of rumor prevention to spread, we also looked into various pertinent approaches, including rumor veracity classification, stance classification, tracking, and combating. We also have created a summary of recent datasets with all the necessary information and analysis. Finally, as part of this survey, we have identified some of the potential research gaps and challenges that need to be addressed in order to develop early, effective methods of rumor control.
Competing Interests: Conflict of interestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this research paper.
(© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)
Databáze: MEDLINE
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