Information Propagation Dynamic Model Based on Rumors, Antirumors, Prom-Rumors, and the Dynamic Game

The propagation of Internet rumors has complex dynamics. The traditional propagation model, based mainly on rumor and antirumor messages, provides a static analysis of the rumor propagation situation. This article introduces the concept of a prom-rumor (promoting rumor) message and thoroughly investigates competition and cooperation among multitype rumor messages. First, we employ a multiple linear regression method to construct the information influence mechanism by extracting network structure, topic content information, and historical behavior information, considering the complexity of spreading multitype compound rumor messages. Then, we introduce game theory and formulate a game strategy to capture the competition and cooperation among rumors, antirumors, and prom-rumors. Furthermore, we consider the interactivity in multitype compound rumor messages and combine the dynamic game to construct the driving mechanism of rumors, antirumors, and prom-rumors. Finally, we define the user-state transfer equation and propose a dynamic model of information propagation based on rumors, antirumors, prom-rumors, and the dynamic game while considering the multidimensionality and polymorphism of multitype compound rumor messages and combining mean-field theory and the infectious disease model. In validation experiments on real-life data, the model accurately captures the game propagation process of the multitype rumors and predicts the propagation trends in rumor networks.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Computational Social Systems
Published
2024-10-10
DOI
https://doi.org/10.1109/tcss.2024.3465023
Citations
13
Primary Topic
Opinion Dynamics and Social Influence
Type
article
Field-Weighted Citation Impact
3.66
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Information Propagation Dynamic Model Based on Rumors, Antirumors, Prom-Rumors, and the Dynamic Game

Rong Wang, Long Gao, Yunpeng Xiao, Xinhong Wu et al.
13 citations
IEEE Transactions on Computational Social Systems
Opinion Dynamics and Social Influence
3.66
article

Information Propagation Dynamic Model Based on Rumors, Antirumors, Prom-Rumors, and the Dynamic Game

Rong Wang, Long Gao, Yunpeng Xiao, Xinhong Wu, Qian Li, Xiaole Guo
article en
13 citations

Abstract

The propagation of Internet rumors has complex dynamics. The traditional propagation model, based mainly on rumor and antirumor messages, provides a static analysis of the rumor propagation situation. This article introduces the concept of a prom-rumor (promoting rumor) message and thoroughly investigates competition and cooperation among multitype rumor messages. First, we employ a multiple linear regression method to construct the information influence mechanism by extracting network structure, topic content information, and historical behavior information, considering the complexity of spreading multitype compound rumor messages. Then, we introduce game theory and formulate a game strategy to capture the competition and cooperation among rumors, antirumors, and prom-rumors. Furthermore, we consider the interactivity in multitype compound rumor messages and combine the dynamic game to construct the driving mechanism of rumors, antirumors, and prom-rumors. Finally, we define the user-state transfer equation and propose a dynamic model of information propagation based on rumors, antirumors, prom-rumors, and the dynamic game while considering the multidimensionality and polymorphism of multitype compound rumor messages and combining mean-field theory and the infectious disease model. In validation experiments on real-life data, the model accurately captures the game propagation process of the multitype rumors and predicts the propagation trends in rumor networks.

IEEE Transactions on Computational Social SystemsVol. 12(1)
Chongqing University of Posts and Telecommunications (CN), University of Hong Kong (HK)
Openalex Percentile: Top 6%
Opinion Dynamics and Social Influence
3.66
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.