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

Simone Raponi, Zeinab Khalifa, Gabriele Oligeri, Roberto Di Pietro

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.
Original languageEnglish (US)
JournalACM Transactions on the Web
Issue number3
StatePublished - Sep 19 2022
Externally publishedYes

ASJC Scopus subject areas

  • Computer Networks and Communications


Dive into the research topics of 'Fake News Propagation: A Review of Epidemic Models, Datasets, and Insights'. Together they form a unique fingerprint.

Cite this