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Fake News Propagation: A Review of Epidemic Models, Datasets, and Insights

Authors:
Simone Raponi

Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

0000-0002-1813-546X
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,
Zeinab Khalifa

Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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,
Gabriele Oligeri

Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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,
Roberto Di Pietro

Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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Authors Info & Claims

ACM Transactions on the Web Volume 16 Issue 3Article No.: 12pp 1–34https://doi.org/10.1145/3522756

Published:19 September 2022Publication History

ACM Transactions on the Web

Abstract

Fake news propagation is a complex phenomenon influenced by a multitude of factors whose identification and impact assessment is challenging. Although many models have been proposed in the literature, the one capturing all the properties of a real fake-news propagation phenomenon is inevitably still missing. Modern propagation models, mainly inspired by old epidemiological models, attempt to approximate the fake-news propagation phenomena by blending psychological factors, social relations, and user behavior.

This work provides an in-depth analysis of the current state of fake-news propagation models supported by real-world datasets. We highlighted similarities and differences in the modeling approaches, wrapping up the main research trends. Propagation models, transitions, network topologies, and performance metrics have been identified and discussed in detail. The thorough analysis we provided in this article, coupled with the highlighted research hints, have a high potential to pave the way for future research in the area.

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Fake News Propagation: A Review of Epidemic Models, Datasets, and Insights

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Published in
ACM Transactions on the Web Volume 16, Issue 3
August 2022
155 pages
ISSN:1559-1131
EISSN:1559-114X
DOI:10.1145/3555790
Editor:
Ryen White
Microsoft Research, USA
Issue’s Table of Contents
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Publication History
- Published: 19 September 2022
- Online AM: 30 March 2022
- Accepted: 27 February 2022
- Revised: 8 December 2021
- Received: 23 May 2021
Published in tweb Volume 16, Issue 3

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Fake News Propagation: A Review of Epidemic Models, Datasets, and Insights

ACM Transactions on the Web

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