Mining large-scale smartphone data for personality studies
Gokul Chittaranjan,Jan Blom,Daniel Gatica-Perez +2 more
- Vol. 17, Iss: 3, pp 433-450
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TLDR
From the analysis, it is shown that several aggregated features obtained from smartphone usage data can be indicators of the Big-Five traits and described a machine learning method to detect the personality trait of a user based on smartphone usage.Abstract:
In this paper, we investigate the relationship between automatically extracted behavioral characteristics derived from rich smartphone data and self-reported Big-Five personality traits (extraversion, agreeableness, conscientiousness, emotional stability and openness to experience). Our data stem from smartphones of 117 Nokia N95 smartphone users, collected over a continuous period of 17 months in Switzerland. From the analysis, we show that several aggregated features obtained from smartphone usage data can be indicators of the Big-Five traits. Next, we describe a machine learning method to detect the personality trait of a user based on smartphone usage. Finally, we study the benefits of using gender-specific models for this task. Apart from a psychological viewpoint, this study facilitates further research on the automated classification and usage of personality traits for personalizing services on smartphones.read more
Citations
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The Mobile Data Challenge: Big Data for Mobile Computing Research
J. Laurila,Daniel Gatica-Perez,Imad Aad,Blom J.,Olivier Bornet,Trinh-Minh-Tri Do,Olivier Dousse,Julien Eberle,Markus Miettinen +8 more
TL;DR: An overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of related mobile data analysis methodologies, is presented.
Journal ArticleDOI
A Survey of Personality Computing
TL;DR: A survey of technologies capable of dealing with human personality, and a conceptual model underlying the three main problems addressed in the literature, namely Automatic Personality Recognition, Automatic Personality Perception and Automatic Personality Synthesis.
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Predicting personality using novel mobile phone-based metrics
TL;DR: This study provides the first evidence that personality can be reliably predicted from standard mobile phone logs with a mean accuracy across traits of 42% better than random, reaching up to 61% accuracy on a three-class problem.
Journal ArticleDOI
Smartphone usage in the 21st century: Who is active on WhatsApp?
Christian Montag,Konrad Błaszkiewicz,Rayna Sariyska,Bernd Lachmann,Ionut Andone,Boris Trendafilov,Mark Eibes,Alexander Markowetz +7 more
TL;DR: The numbers on smartphone usage in the present study show that the smartphone dominates the authors' daily life, and WhatsApp is a driving force, here.
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Big Data sources and methods for social and economic analyses
TL;DR: This paper develops a Big Data architecture that properly integrates most of the non-traditional information sources and data analysis methods in order to provide a specifically designed system for forecasting social and economic behaviors, trends and changes.
References
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