Realistic modelling of information spread using peer-to-peer diffusion patterns

Bin Zhou (), Sen Pei (), Lev Muchnik, Xiangyi Meng, Xiaoke Xu, Alon Sela, Shlomo Havlin and H. Eugene Stanley
Additional contact information
Bin Zhou: Jiangsu University of Science and Technology
Sen Pei: Columbia University
Lev Muchnik: The Hebrew University of Jerusalem
Xiangyi Meng: Boston University
Xiaoke Xu: Dalian Minzu University
Alon Sela: Ariel University
Shlomo Havlin: Boston University
H. Eugene Stanley: Boston University

Nature Human Behaviour, 2020, vol. 4, issue 11, 1198-1207

Abstract: Abstract In computational social science, epidemic-inspired spread models have been widely used to simulate information diffusion. However, recent empirical studies suggest that simple epidemic-like models typically fail to generate the structure of real-world diffusion trees. Such discrepancy calls for a better understanding of how information spreads from person to person in real-world social networks. Here, we analyse comprehensive diffusion records and associated social networks in three distinct online social platforms. We find that the diffusion probability along a social tie follows a power-law relationship with the numbers of disseminator’s followers and receiver’s followees. To develop a more realistic model of information diffusion, we incorporate this finding together with a heterogeneous response time into a cascade model. After adjusting for observational bias, the proposed model reproduces key structural features of real-world diffusion trees across the three platforms. Our finding provides a practical approach to designing more realistic generative models of information diffusion.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.nature.com/articles/s41562-020-00945-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nathum:v:4:y:2020:i:11:d:10.1038_s41562-020-00945-1

Ordering information: This journal article can be ordered from
https://www.nature.com/nathumbehav/

DOI: 10.1038/s41562-020-00945-1

Access Statistics for this article

Nature Human Behaviour is currently edited by Stavroula Kousta

More articles in Nature Human Behaviour from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().