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ABSTRACT: Mobile phones are quickly becoming the primary source for social, behavioral,
and environmental sensing and data collection. Today's smartphones are equipped
with increasingly more sensors and accessible data types that enable the
collection of literally dozens of signals related to the phone, its user, and
its environment. A great deal of research effort in academia and industry is
put into mining this raw data for higher level sense-making, such as
understanding user context, inferring social networks, learning individual
features, predicting outcomes, and so on. In this work we investigate the
properties of learning and inference of real world data collected via mobile
phones over time. In particular, we look at the dynamic learning process over
time, and how the ability to predict individual parameters and social links is
incrementally enhanced with the accumulation of additional data. To do this, we
use the Friends and Family dataset, which contains rich data signals gathered
from the smartphones of 140 adult members of a young-family residential
community for over a year, and is one of the most comprehensive mobile phone
datasets gathered in academia to date. We develop several models that predict
social and individual properties from sensed mobile phone data, including
detection of life-partners, ethnicity, and whether a person is a student or
not. Then, for this set of diverse learning tasks, we investigate how the
prediction accuracy evolves over time, as new data is collected. Finally, based
on gained insights, we propose a method for advance prediction of the maximal
learning accuracy possible for the learning task at hand, based on an initial
set of measurements. This has practical implications, like informing the design
of mobile data collection campaigns, or evaluating analysis strategies.
11/2011;
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ABSTRACT: Sleep and mood problems have a considerable public health impact with serious societal and significant financial effects. In this work, we study the relationship between these factors in the everyday life of healthy young adults. More importantly, we look at these factors from a social perspective, studying the impact that couples have on each other and the role that face-to-face interactions play. We find that there is a significant bi-directional relationship between mood and sleep. More interestingly, we find that the spouse's sleep and mood may have an effect on the subject's mood and sleep. Further, we find that subjects whose sleep is significantly correlated with mood tend to be more sociable. Finally, we observe that less sociable subjects show poor mood more often than their more sociable contemporaries. These novel insights, especially those involving sociability, measured from quantified face-to-face interaction data gathered through smartphones, open up several avenues to enhance public health research through the use of latest wireless sensing technologies.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:5267-70.
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ABSTRACT: We have carefully instrumented a large portion of the population living in a
university graduate dormitory by giving participants Android smart phones
running our sensing software. In this paper, we propose the novel problem of
predicting mobile application (known as "apps") installation using social
networks and explain its challenge. Modern smart phones, like the ones used in
our study, are able to collect different social networks using built-in
sensors. (e.g. Bluetooth proximity network, call log network, etc) While this
information is accessible to app market makers such as the iPhone AppStore, it
has not yet been studied how app market makers can use these information for
marketing research and strategy development. We develop a simple computational
model to better predict app installation by using a composite network computed
from the different networks sensed by phones. Our model also captures
individual variance and exogenous factors in app adoption. We show the
importance of considering all these factors in predicting app installations,
and we observe the surprising result that app installation is indeed
predictable. We also show that our model achieves the best results compared
with generic approaches: our results are four times better than random guess,
and predict almost 45% of all apps users install with almost 45% precision (F1
score= 0.43).
06/2011;
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PASSAT/SocialCom 2011, Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom), Boston, MA, USA, 9-11 Oct., 2011; 01/2011
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PASSAT/SocialCom 2011, Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Confernece on Social Computing (SocialCom), Boston, MA, USA, 9-11 Oct., 2011; 01/2011
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UbiComp 2011: Ubiquitous Computing, 13th International Conference, UbiComp 2011, Beijing, China, September 17-21, 2011, Proceedings; 01/2011
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IEEE Intelligent Systems. 01/2011; 26:22-30.
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Pervasive and Mobile Computing. 01/2011; 7:643-659.
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ABSTRACT: In this paper we discuss the threat of malware targeted at extracting
information about the relationships in a real-world social network as well as
characteristic information about the individuals in the network, which we dub
Stealing Reality. We present Stealing Reality, explain why it differs from
traditional types of network attacks, and discuss why its impact is
significantly more dangerous than that of other attacks. We also present our
initial analysis and results regarding the form that an SR attack might take,
with the goal of promoting the discussion of defending against such an attack,
or even just detecting the fact that one has already occurred.
10/2010;
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ABSTRACT: In recent years, reality mining experiments have provided several novel insights into human social behavior that would not have been possible without the novel use of smartphone sensing. In this work, we leverage the latest reality mining experiment to study social behavior from a public health perspective. In particular, we focus on sleep and mood as they have a considerable public health impact with serious societal and significant financial effects. We endeavor to explore and uncover the associations between sleep, mood and sociability by studying a population of healthy young adults going about their everyday life. We find significant associations between sleep and mood, reiterating observations in the literature. More importantly, we find that individuals with lower overall sociability tend to report poor mood more often, a statistically significant observation. In addition, we also uncover associations between daily sociability and sleep, a previously unreported observation. These results demonstrate the potential of reality mining studies for studying the sociological aspects of significant public health problems. Further, we hope that our work will provide the impetus for larger studies validating some of these observations and ultimately result in behavioral interventions that can improve public health through better social interaction.
