Networks rely on broadcasts and multicasts for some of the most basic services such as auto-configuration. In the recent past, application layer protocols have increasingly made use of the broadcast mechanism. Examples of these applications include Dropbox, Spotify or BitTorrent Sync. Given that broadcasts can be seen by every device in a broadcast domain, information that can be gleaned from this traffic is trivially accessible by a passive observer. Therefore, an obvious question is: what does broadcast and multicast traffic reveal about a device, a user or a group in a network? To answer this question, the broadcast traffic of two fairly large wireless networks was analyzed. One of these networks was the campus network of a university which was analyzed for a period of six months. Also, two SSIDs of the IETF meeting network in Yokohama in November 2015 were analyzed for a period of about 36 hours. In addition to a general analysis of the composition of the daily broadcast traffic such as protocols observed, the number of devices, the peak times of user activity etc., a more in-depth analysis of a few protocols was carried out in order to identify users and their relation to each other. In other words, we used the available broadcast data to show that it is possible to generate a social graph of the network’s users base, which e.g. helps to identify groups among students, their course of study, their online times and other personal information. We have verified the correctness of our inferred social graph by asking students to confirm our findings. None of the observed broadcast protocols alone is to blame for the above and there is no easy technical solution to the problem while retaining the benefits of the broadcast protocols. However, there is a simple yet effective countermeasure against this kind of analysis which is non-technical and ‘only’ requires changing user behavior.