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Knowledge of information cascades through social networks facilitates strategic gossip

Nature Human Behaviour (2025)Cite this article

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Abstract

Social networks are composed of many ties among many individuals. These ties enable the spread of information through a network, including gossip, which comprises a sizeable share of daily conversation. Given the number of possible connections between people in even the smallest networks, a formidable challenge is how to strategically gossip—to disseminate information as widely as possible without the target of the gossip finding out. Here we find that people achieve this goal by leveraging knowledge about topological properties, specifically, social distance and popularity, using a gossip-sharing task in artificial social networks (experiments 1–3, N = 568). We find a similar pattern of behaviour in a real-world social network (experiment 4, N = 187), revealing the power of these topological properties in predicting information flow, even in much noisier, complex environments. Computational modelling suggests that these adaptive social behaviours rely on mental representations of information cascades through the social network.

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Fig. 1: Network and task structure used in online experiments.
Fig. 2: Likelihood of gossiping in experiments 1 and 2.
Fig. 3: Likelihood of gossiping in experiment 3.
Fig. 4: Predictions about information flow in a large, real-world social network (experiment 4).
Fig. 5: Computational modelling results.

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Data availability

The deidentified data are publicly available at https://osf.io/4kbsj/. The stimuli used in the artificial social networks were sources from the Chicago Face Database available at https://www.chicagofaces.org/.

Code availability

The code for reproducing analyses is publicly available at https://osf.io/4kbsj/.

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Acknowledgements

We thank A. Maddah for developing some of the task code used in experiments 1–3. We thank J.-Y. Son and I. Aslarus for assisting with data collection for experiment 4. We also thank J.-Y. Son and Y.-F. Hu for helpful advice and discussions on computational modelling. This work is supported by the National Science Foundation (award 2123469 to O.F.H. and A.B.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Apoorva Bhandari, Oriel FeldmanHall.

Authors and Affiliations

  1. Department of Cognitive and Psychological Sciences, Brown University, Providence, RI, USA

    Alice Xia, Yi Yang Teoh, Matthew R. Nassar, Apoorva Bhandari & Oriel FeldmanHall

  2. Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA

    Matthew R. Nassar & Oriel FeldmanHall

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  1. Alice Xia
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  2. Yi Yang Teoh
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Contributions

A.B. and O.F.H. contributed equally to this work. Conceptualization: A.X., A.B. and O.F.H. Formal analysis: A.X. and Y.Y.T. Funding acquisition: O.F.H. Investigation: A.X. Methodology: A.X., Y.Y.T., M.R.N., A.B. and O.F.H. Supervision: O.F.H. and A.B. Writing—original draft: A.X., A.B. and O.F.H. Writing—review and editing: A.X., Y.Y.T., M.R.N., A.B. and O.F.H.

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Correspondence to Oriel FeldmanHall.

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Xia, A., Teoh, Y.Y., Nassar, M.R. et al. Knowledge of information cascades through social networks facilitates strategic gossip. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02241-2

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