Anomalous structure and dynamics in news diffusion among heterogeneous individuals

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Abstract

Previous research has suggested that well-connected nodes in a network (commonly referred to as hubs) are better at spreading information than those with fewer connections (ordinary users). Here we investigate the roles of nodes with different numbers of connections by studying how people share news online. Quantitative analysis shows that users without many connections can sometimes spread news more effectively than well-connected users when the diffusion pattern has dendrite-like paths that reach far into the network, leading to a non-Gaussian distance distribution. When the hubs dominate, however, the distribution is Gaussian. Enhanced interactions among ordinary users are the key to the emergence of non-Gaussian characteristics. Finally, we introduce a message-passing model that reproduces the observed diffusion features. This model shows that patterns dominated by either hubs or ordinary users can be clearly demarcated by measuring the average number of forwards.

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Fig. 1: Measuring the direction of information flow.
Fig. 2: Roles of the two types of users in real-world message spreading.
Fig. 3: Structure of news diffusion networks.
Fig. 4: Illustration of the message-passing model with heterogeneous influence strength.
Fig. 5: Reproducing the spreading patterns of news1, news2 and news3.
Fig. 6: Diffusion networks produced by the model.
Fig. 7: Parameter dependence of the diffusion network structure and diffusion patterns.

Data availability

The headlines of all the news stories were collected from the hot news website of Sina News Center (http://news.sina.com.cn/hotnews/). We asked a commercial institution (https://www.shenjianshou.cn/) to help us collect data on Sina Weibo (http://weibo.com/). The downloaded data include all the posts for each piece of news (that is, their respective user interactions and the follower counts of the users) that are publicly available on Sina Weibo (users with privacy restrictions are not included in the dataset). The data that support the findings of this study are available at https://www.researchgate.net/publication/328783349_data_of_news.

Code availability

Code for the data analysis and model simulation is available at https://www.researchgate.net/publication/328783349_data_of_news. The code was run using Python 238 and Matlab R2015b39 for data analysis and Matlab R2015b for model simulation.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant nos. 11775034 and 11375093). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

X.W., Y.L. and J.X. contributed equally to all parts of the research and writing.

Correspondence to Xiaochen Wang or Jinghua Xiao.

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The authors declare no competing interests.

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Supplementary Information

Supplementary Information

Supplementary Methods 1−8, Supplementary Notes 1−5, Supplementary Tables 1−6, Supplementary Figures 1−17 and Supplementary References.

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Wang, X., Lan, Y. & Xiao, J. Anomalous structure and dynamics in news diffusion among heterogeneous individuals. Nat Hum Behav 3, 709–718 (2019) doi:10.1038/s41562-019-0605-7

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