We explore the social and contextual factors that influence the outcome of person-to-person music recommendations and discovery. Specifically, we use data from Spotify to investigate how a link sent from one user to another results in the receiver engaging with the music of the shared artist. We consider several factors that may influence this process, such as the strength of the sender-receiver relationship, the user's role in the Spotify social network, their music social cohesion, and how similar the new artist is to the receiver's taste. We find that the receiver of a link is more likely to engage with a new artist when (1) they have similar music taste to the sender and the shared track is a good fit for their taste, (2) they have a stronger and more intimate tie with the sender, and (3) the shared artist is popular amongst the receiver's connections. Finally, we use these findings to build a Random Forest classifier to predict whether a shared music track will result in the receiver's engagement with the shared artist. This model elucidates which type of social and contextual features are most predictive, although peak performance is achieved when a diverse set of features are included. These findings provide new insights into the multifaceted mechanisms underpinning the interplay between music discovery and social processes.

Through their normal operation, cellular networks are a repository of continuous location information from their subscribed devices. Such information, however, comes at a coarse granularity both in terms of space, as well as time. For otherwise inactive devices, location information can be obtained at the granularity of the associated cellular sector, and at infrequent points in time, that are sensitive to the structure of the network itself, and the level of mobility of the device. In this paper, we are asking the question of whether such sparse information can help to identify the paths followed by mobile connected devices throughout the day. If such a task is possible, then we would not only enable continuous mobility path estimation for smartphones, but also for the millions of future connected "things".

People manage a spectrum of identities in cyber domains. Profiling individuals and assigning them to distinct groups or classes have potential applications in targeted services, online fraud detection, extensive social sorting, and cyber-security. This paper presents the Uncertainty of Identity Toolset, a framework for the identification and profiling of users from their social media accounts and e-mail addresses. More specifically, in this paper we discuss the design and implementation of two tools of the framework. The Twitter Geographic Profiler tool builds a map of the ethno-cultural communities of a person's friends on Twitter social media service. The E-mail Address Profiler tool identifies the probable identities of individuals from their e-mail addresses and maps their geographical distribution across the UK. To this end, this paper presents a framework for profiling the digital traces of individuals.

The importance of spatial information in Online Social Networks is increasing at a fast pace. The number of users regularly accessing services from their phones is rising and, therefore, local information is becoming more and more important, for example in targeted marketing and personalized services. In particular, news, from gossips to security alerts, are daily spread across cities through social networks. Content produced by users is consumed by their friends or followers, whose locations can be known or inferred. The spatial location of users' social connections strongly affects the areas where such information will be disseminated. As a consequence, some users can deliver content to a certain geographic area more easily and efficiently than others, for example because they have a larger number of friends in that area.

P2P communication paradigm is a successful solution to the problem of resources sharing as shown by the numerous real overlay networks present on Internet. One of the issue of P2P networks is how a resource shared by a peer can be made known to the other peers or, in other words, how to advertise a resource on the network. In this paper we propose an advertising method for PROSA, a P2P architecture inspired by social relationships. In the paper we show that the introduction of our resources advertising method improves PROSA performance with a low overhead.