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Review
. 2023;26(3):1709-1735.
doi: 10.1007/s10586-022-03706-z. Epub 2022 Aug 23.

A survey of uncover misleading and cyberbullying on social media for public health

Omar Darwish  1 , Yahya Tashtoush  2 , Amjad Bashayreh  2 , Alaa Alomar  2 , Shahed Alkhaza'leh  2 , Dirar Darweesh  2
Affiliations
Review

A survey of uncover misleading and cyberbullying on social media for public health

Omar Darwish et al. Cluster Comput. 2023.

Abstract

Misleading health information is a critical phenomenon in our modern life due to advance in technology. In fact, social media facilitated the dissemination of information, and as a result, misinformation spread rapidly, cheaply, and successfully. Fake health information can have a significant effect on human behavior and attitudes. This survey presents the current works developed for misleading information detection (MLID) in health fields based on machine learning and deep learning techniques and introduces a detailed discussion of the main phases of the generic adopted approach for MLID. In addition, we highlight the benchmarking datasets and the most used metrics to evaluate the performance of MLID algorithms are discussed and finally, a deep investigation of the limitations and drawbacks of the current progressing technologies in various research directions is provided to help the researchers to use the most proper methods in this emerging task of MLID.

Keywords: BERT; COVID-19; Deep learning; Disinformation; Machine learning; Misinformative; Misleading information.

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Conflict of interest statement

Conflict of interestThe authors declare there were no conflicts of interest.

Figures

Fig. 1
Fig. 1
A representation of recent studies on misleading information topic from 2015 to 2022
Fig. 2
Fig. 2
Framework for detecting misleading information
See this image and copyright information in PMC

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