A dissemination model based on psychological theories in complex social networks

Tianyi Luo, Zhidong Cao, Daniel Zeng, Qingpeng Zhang

Research output: Contribution to journalArticlepeer-review


Information spread on social media has been extensively studied through both model-driven theoretical research and data-driven case studies. Recent empirical studies have analyzed the differences and complexity of information dissemination, but theoretical explanations of its characteristics from a modeling perspective are under-researched. To capture the complex patterns of the information dissemination mechanism, we propose a resistant linear threshold (RLT) dissemination model based on psychological theories and empirical findings. In this paper, we validate the RLT model on three types of networks and then quantify and compare the dissemination characteristics of the simulation results with those from the empirical results. In addition, we examine the factors affecting dissemination. Finally, we perform two case studies of 2019 novel coronavirus (COVID-19)-related information dissemination. The dissemination characteristics derived by the simulations are consistent with the empirical research. These results demonstrate that the RLT model is able to capture the patterns of information dissemination on social media and thus provide model-driven insights into the interpretation of public opinion, rumor control, and marketing strategies on social media.

Original languageEnglish (US)
JournalIEEE Transactions on Cognitive and Developmental Systems
StateAccepted/In press - 2021
Externally publishedYes


  • Attenuation
  • COVID-19
  • Information dissemination
  • Integrated circuit modeling
  • Interference
  • Psychology
  • Resistance
  • Social networking (online)
  • complex networks
  • differential dissemination
  • dissemination mechanism.
  • psychology

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


Dive into the research topics of 'A dissemination model based on psychological theories in complex social networks'. Together they form a unique fingerprint.

Cite this