Applications such as Facebook, Twitter and Foursquare brought the massification of personal short messages, distributed in (soft) realtime on the Internet to a large number of users. These messages are complemented with rich contextual information such as the identity, time and location of the person sending the message. Such contextual messages raise serious concerns in terms of scalability and delivery delay; this results not only from their huge number but also because the set of user

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... nts changes for each message (as their interests continuously change), preventing the use of well-know solutions such as pub-sub and multicast trees. This leads to the use of non-scalable broadcast based solutions or pointto-point messaging. We propose Radiator, a middleware to assist application programmers implementing efficient context propagation mechanisms on their applications. Based on each user current context, Radiator continuously adapts each message propagation path and delivery delay, making an efficient use of network bandwidth, arguably the biggest bottleneck in the deployment of large-scale context propagation systems. Our experimental results demonstrate a 20x reduction on consumed bandwidth without affecting the real-time usefulness of the propagated messages.