TY - JOUR
T1 - Fake News Propagation: A Review of Epidemic Models, Datasets, and Insights
AU - Raponi, Simone
AU - Khalifa, Zeinab
AU - Oligeri, Gabriele
AU - Di Pietro, Roberto
N1 - Generated from Scopus record by KAUST IRTS on 2023-09-20
PY - 2022/9/19
Y1 - 2022/9/19
N2 - 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.
AB - 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.
UR - https://dl.acm.org/doi/10.1145/3522756
UR - http://www.scopus.com/inward/record.url?scp=85137044472&partnerID=8YFLogxK
U2 - 10.1145/3522756
DO - 10.1145/3522756
M3 - Article
SN - 1559-114X
VL - 16
JO - ACM Transactions on the Web
JF - ACM Transactions on the Web
IS - 3
ER -