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Deep Learning Models to Detect Online False Information: a Systematic Literature Review

EasyChair Preprint no. 6234

8 pagesDate: August 5, 2021


The amount of disseminated information from online content volume is increasing rapidly including trusted and untrusted information published by different sources. To counter this problem, we need a comprehensive knowledge of existing methods and techniques emerging in the area of False News Detection (FND).This research survey provides a comprehensive review of most effective Deep Learning (DL) models used to detect false news and information. We are focusing in DL developed models and techniques used the textual published content and perform FND based on content features. Considering published articles in the last five years starting from 2017 onward. In this research paper, the published articles about proposing and developing FND based DL models are included whether the dataset are collected from social platforms or extracted from other news sources. In addition, this research study helps the researchers to have a complete view of FND based DL models gaps, how they can be improved and what are the developed DL models that have been proposed in the field of false information detection.

Keyphrases: deep learning, disinformation, fake news, false information, misinformation

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Asmaa Seyam and Ali Bou Nassif and Manar Abu Talib and Qassim Nasir and Bushra Alblooshi},
  title = {Deep Learning Models to Detect Online False Information: a Systematic Literature Review},
  howpublished = {EasyChair Preprint no. 6234},

  year = {EasyChair, 2021}}
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