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.
Bibliographical noteFunding Information:
B.Z. is funded by Natural Science Foundation of China (grant no. 61503159) and Jiangsu University Overseas Training Programme. S.P. is supported by NIH NIGMS grant no. 5U01GM110748. L.M. is supported by Israel Science Foundation grant no. 1777/17. X.M. and H.E.S. are supported by NSF Grant PHY-1505000 and DTRA grant no. HDTRA-1-14-1-0017. X.X. is supported by National Natural Science Foundation of China (grant no. 61773091). A.S. wishes to thank the Ariel Cyber Innovation Centre in conjunction with the Israel National directorate in the Prime Minister’s Office for their support. S.H. thanks the Italian Ministry of Foreign Affairs and International Cooperation jointly with the Israeli Ministry of Science, Technology, and Space (MOST); the Israel Science Foundation, ONR, the Japan Science Foundation with MOST, BSF-NSF, ARO, the Bar-Ilan University Centre for Research in Applied Cryptography and Cyber Security and DTRA (grant no. HDTRA-1-19-1-0016) for financial support.
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