期刊
NATURE HUMAN BEHAVIOUR
卷 4, 期 11, 页码 1198-1207出版社
NATURE RESEARCH
DOI: 10.1038/s41562-020-00945-1
关键词
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资金
- Natural Science Foundation of China [61503159]
- Jiangsu University Overseas Training Programme
- NIH NIGMS [5U01GM110748]
- Israel Science Foundation [1777/17]
- NSF [PHY-1505000]
- DTRA [HDTRA-1-14-1-0017, HDTRA-1-19-1-0016]
- National Natural Science Foundation of China [61773091]
- Ariel Cyber Innovation Centre
- Italian Ministry of Foreign Affairs and International Cooperation
- Israeli Ministry of Science, Technology, and Space (MOST)
- ONR
- Japan Science Foundation
- MOST
- BSF-NSF
- ARO
- Bar-Ilan University Centre for Research in Applied Cryptography and Cyber Security
- Israel National directorate in the Prime Minister's Office
Zhou, Pei et al. develop a more realistic information cascade model that reproduces key structures of real-world diffusion trees in distinct social platforms by combining a peer-to-peer diffusion pattern with a correction for observational bias. 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.
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