Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining communities and their relationships in blogs: A study of online hate groups
International Journal of Human-Computer Studies
Identity management: multiple presentations of self in facebook
Proceedings of the 2007 international ACM conference on Supporting group work
Social context and communication channels choice among adolescents
Computers in Human Behavior
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Cluestr: mobile social networking for enhanced group communication
Proceedings of the ACM 2009 international conference on Supporting group work
When social networks cross boundaries: a case study of workplace use of facebook and linkedin
Proceedings of the ACM 2009 international conference on Supporting group work
All My People Right Here, Right Now: management of group co-presence on a social networking site
Proceedings of the ACM 2009 international conference on Supporting group work
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Feasibility of structural network clustering for group-based privacy control in social networks
Proceedings of the Sixth Symposium on Usable Privacy and Security
A large scale study of text-messaging use
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
Bridging the gap between physical location and online social networks
Proceedings of the 12th ACM international conference on Ubiquitous computing
Social sensing for epidemiological behavior change
Proceedings of the 12th ACM international conference on Ubiquitous computing
Faceted identity, faceted lives: social and technical issues with being yourself online
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 13th international conference on Ubiquitous computing
An investigation into facebook friend grouping
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
Modeling the co-evolution of behaviors and social relationships using mobile phone data
Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia
How many makes a crowd? on the evolution of learning as a factor of community coverage
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Mobile Human Network Management and Recommendation by Probabilistic Social Mining
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Human interaction discovery in smartphone proximity networks
Personal and Ubiquitous Computing
Mining large-scale smartphone data for personality studies
Personal and Ubiquitous Computing
Enabling an ecosystem of personal behavioral data
Proceedings of the adjunct publication of the 26th annual ACM symposium on User interface software and technology
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
Visual analysis of social networks in space and time using smartphone logs
Pervasive and Mobile Computing
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People engage with many overlapping social networks and enact diverse social roles across different facets of their lives. Unfortunately, many online social networking services reduce most people's contacts to "friend". A richer computational model of relationships would be useful for a number of applications such as managing privacy settings and organizing communications. In this paper, we take a step towards a richer computational model by using call and text message logs from mobile phones to classifying contacts according to life facet (family, work, and social). We extract various features such as communication intensity, regularity, medium, and temporal tendency, and classify the relationships using machine-learning techniques. Our experimental results on 40 users showed that we could classify life facets with up to 90.5% accuracy. The most relevant features include call duration, channel selection, and time of day for the communication.