Misinformation Detection Using Deep Learning

Michail Tsikerdekis, Sherali Zeadally

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years, we have witnessed growing interest in using deep learning to detect misinformation. This increased attention is being driven by deep learning technologies ability to accurately detect this misinformation. However, there is a diverse array of content that can be considered misinformation, such as fake news and satire. Similarly, in the field of deep learning, there are several architectures with variable efficacy depending on the context and data involved. This study aims to highlight the various types of misinformation attacks and deep learning architectures that are used to detect them. Based on our selection of the recent literature, we present a classification of deep learning approaches and their relative effectiveness in detecting misinformation, along with their limitations in terms of accuracy as well as computational overhead. Finally, we discuss some challenges and limitations that arise FROM the use of deep learning architectures in misinformation detection.

Original languageEnglish
Pages (from-to)57-63
Number of pages7
JournalIT Professional
Volume25
Issue number5
DOIs
StatePublished - Sep 1 2023

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Science Applications

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