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Detecting and modelling real percolation and phase transitions of information on social media


It is widely believed that information spread on social media is a percolation process, with parallels to phase transitions in theoretical physics. However, evidence for this hypothesis is limited, as phase transitions have not been directly observed in any social media. Here, through an analysis of 100 million Weibo and 40 million Twitter users, we identify percolation-like spread and find that it happens more readily than current theoretical models would predict. The lower percolation threshold can be explained by the existence of positive feedback in the coevolution between network structure and user activity level, such that more-active users gain more followers. Moreover, this coevolution induces an extreme imbalance in users’ influence. Our findings indicate that the ability of information to spread across social networks is higher than expected, with implications for many information-spread problems.

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Fig. 1: Unexpectedly low threshold of information cascades in Weibo.
Fig. 2: Empirical results and data-driven percolation model in Weibo.
Fig. 3: Results of the data-driven percolation model in Twitter.

Data availability

The data for this study are available at The network is very large. You also can find detailed information on how to download the data at Source data are provided with this paper.

Code availability

The code for this study is available at


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We thank L. Feng and W. Liu for their very helpful discussions. This work was supported by the Natural Science Foundation of Guangdong for Distinguished Youth Scholar, Guangdong Provincial Department of Science and Technology (grant no. 2020B1515020052), Guangdong High-Level Personnel of Special Support Program, Young TopNotch Talents in Technological Innovation (grant no. 2019TQ05X138), the National Natural Science Foundation of China (grant nos 61903385, 61773412, U1911201, U1711265 and 61971454) and the National Key R&D Program of China (grant no. 2018AAA0101203). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations



Y.H. conceived the project. Y.H., J.X. and G.Y. designed the experiments. J.X. performed the experiments and numerical modelling. J.X. and Y.H. solved the model. J.X., F.M., J.S., X.M., G.Y. and Y.H. discussed and analysed the results. J.X., G.Y. and Y.H. wrote the manuscript.

Corresponding author

Correspondence to Yanqing Hu.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Michael Danziger, Maksim Kitsak and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Relative error of estimated retweet probability \(\frac{{\hat \beta - \beta }}{\beta }\) as a function of cascade size P.

Each dot represents one simulation of data-driven model M2 in Weibo network 2014. \(\hat \beta\) represents the estimated retweet probability by \(\hat \beta = \frac{{P_\infty }}{{P_e}}\) and the real retweet probability is β=0.001 for all dots. Both axis are plotted in log scale.

Source data

Extended Data Fig. 2 Global state and localized state of information spreading.

a, Schematic of information cascade in directed network. The spreading in undirected network is a specific scenario in which PGIN(β)=PGOUT(β)=PGSCC(β). b, The cascade sizes are obtained from simulations using data-driven percolation model in Weibo network. The x-axis is plotted in log scale. Each blue point represents one realization. The box region with dashed purple line is most useful for observing phase transitions.

Source data

Extended Data Fig. 3 Cascade size distribution ps of uniform models in Weibo.

a-c, The cascade size distribution ps with different retweet probabilities β or λ. The red crosses and yellow circles represent simulation results. The theoretical results are numerical solutions with quadruple-precision floating-point format. d, The probability of large cascade size fluctuation below the percolation threshold. The values of probability that P happens in the boxes are theoretical results. Each blue dot represents a real cascade. βc=2.64×10−3 is the percolation threshold of uniform site percolation, which equals to the threshold of bond model. We use log scale for both axis in A-C, and set only x-axis as log scale in D.

Source data

Extended Data Fig. 4 Distributions of relative error between empirical cascades and models.

a, Distribution of relative error in data-driven percolation model. b, Distribution of relative error in uniform percolation model.

Source data

Supplementary information

Supplementary Information

Supplementary Methods 1–5, Supplementary Results 1–6, Supplementary Discussion 1–8, Supplementary Figs. 1–32, Supplementary Tables 1–11 and Supplementary References.

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Source data

Source Data Fig. 1

Statistical source data and simulation results.

Source Data Fig. 2

Statistical source data, simulation results and theoretical results.

Source Data Fig. 3

Statistical source data, simulation results and theoretical results.

Source Data Extended Data Fig. 1

Simulation results.

Source Data Extended Data Fig. 2

Simulation results.

Source Data Extended Data Fig. 3

Statistical source data, simulation results and theoretical results.

Source Data Extended Data Fig. 4

Statistical source data and simulation results.

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Xie, J., Meng, F., Sun, J. et al. Detecting and modelling real percolation and phase transitions of information on social media. Nat Hum Behav 5, 1161–1168 (2021).

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