Machine Learning
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
EACE '05 Proceedings of the 2005 annual conference on European association of cognitive ergonomics
An empirical evaluation of supervised learning in high dimensions
Proceedings of the 25th international conference on Machine learning
Size Matters: Variation in Personal Network Size, Personality and Effect on Information Transmission
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The behavior chain for online participation: how successful web services structure persuasion
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
Social sensing for epidemiological behavior change
Proceedings of the 12th ACM international conference on Ubiquitous computing
A survey of mobile phone sensing
IEEE Communications Magazine
Towards a psychographic user model from mobile phone usage
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
Social fMRI: Investigating and shaping social mechanisms in the real world
Pervasive and Mobile Computing
The personality of popular facebook users
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Personality and persuasive technology: an exploratory study on health-promoting mobile applications
PERSUASIVE'10 Proceedings of the 5th international conference on Persuasive Technology
Mining large-scale smartphone data for personality studies
Personal and Ubiquitous Computing
User demographics prediction based on mobile data
Pervasive and Mobile Computing
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In this work, we investigate the relationships between social network structure and personality; we assess the performances of different subsets of structural network features, and in particular those concerned with ego-networks, in predicting the Big-5 personality traits. In addition to traditional survey-based data, this work focuses on social networks derived from real-life data gathered through smartphones. Besides showing that the latter are superior to the former for the task at hand, our results provide a fine-grained analysis of the contribution the various feature sets are able to provide to personality classification, along with an assessment of the relative merits of the various networks exploited.