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35 reviewsSocial 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 fnding out. Here we fnd that people achieve this goal by leveraging knowledge about topological properties, specifcally, social distance and popularity, using a gossip-sharing task in artifcial social networks (experiments 1–3, N = 568). We fnd 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 fow, 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.