Familiar Strangers detection in online social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Online social networks and microblogging platforms have collected a huge number of users this last decade. On such platforms, traces of activities are automatically recorded and stored on remote servers. Open data deriving from these traces of interactions represent a major opportunity for social network analysis and mining. This leads to important challenges when trying to understand and analyse these large-scale networks better. Recently, many sociological concepts such as friendship, community, trust and reputation have been transposed and integrated into online social networks. The recent success of mobile social networks and the increasing number of nomadic users of online social networks can contribute to extending the scope of these concepts. In this paper, we transpose the notion of the Familiar Stranger, which is a sociological concept introduced by Stanley Milgram. We propose a framework particularly adapted to online platforms that allows this concept to be defined. Various application fields may be considered: entertainment, services, homeland security, etc. To perform the detection task, we address the concept of familiarity based on spatio-temporal and attribute similarities. The paper ends with a case study of the well-known microblogging platform Twitter.