Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The familiar stranger: anxiety, comfort, and play in public places
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Detection of Bloggers' Interests: Using Textual, Temporal, and Interactive Features
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Are you moved by your social network application?
Proceedings of the first workshop on Online social networks
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
FriendSensing: recommending friends using mobile phones
Proceedings of the third ACM conference on Recommender systems
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Discovering users' topics of interest on twitter: a first look
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
A Unified Framework for Link Recommendation Using Random Walks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Geo-Friends Recommendation in GPS-based Cyber-physical Social Network
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
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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.