12,666 research outputs found
Pervasive sensing to model political opinions in face-to-face networks
Exposure and adoption of opinions in social networks are
important questions in education, business, and government. We de-
scribe a novel application of pervasive computing based on using mobile
phone sensors to measure and model the face-to-face interactions and
subsequent opinion changes amongst undergraduates, during the 2008
US presidential election campaign. We nd that self-reported political
discussants have characteristic interaction patterns and can be predicted
from sensor data. Mobile features can be used to estimate unique individ-
ual exposure to di erent opinions, and help discover surprising patterns
of dynamic homophily related to external political events, such as elec-
tion debates and election day. To our knowledge, this is the rst time
such dynamic homophily e ects have been measured. Automatically esti-
mated exposure explains individual opinions on election day. Finally, we
report statistically signi cant di erences in the daily activities of individ-
uals that change political opinions versus those that do not, by modeling
and discovering dominant activities using topic models. We nd people
who decrease their interest in politics are routinely exposed (face-to-face)
to friends with little or no interest in politics.U.S. Army Research Laboratory (Cooperative Agreement No. W911NF-09-2-0053)United States. Air Force Office of Scientific Research (Award No. FA9550-10-1-0122)Swiss National Science Foundatio
Sensing the 'Health State' of our Society
Mobile phones are a pervasive platform for opportunistic sensing of behaviors and opinions. We
show that location and communication sensors can be used to model individual symptoms, long-term health outcomes, and diff usion of opinions in society. For individuals, phone-based features can be used to predict changes in health, such as common colds, influenza, and stress, and automatically identify symptomatic days. For longer-term health outcomes such as obesity, we fi nd that weight changes of participants are correlated with exposure to peers who gained weight in the same period, which is in direct contrast to currently accepted theories of social contagion. Finally, as a proxy for understanding health education we examine change in political opinions during the 2008 US presidential election campaign. We discover dynamic patterns of homophily and use topic models (Latent Dirchlet Allocation) to understand the link between specfii c behaviors and changes in political opinions.United States. Army Research Laboratory (Cooperative Agreement Number W911NF-09-2-0053)United States. Air Force Office of Scientific Research (Award Number FA9550-10-1-0122)Swiss National Science Foundation (MULTI Project)United States. Air Force Office of Scientific Research (Award Number FA9550-08-1- 0132
Robust modeling of human contact networks across different scales and proximity-sensing techniques
The problem of mapping human close-range proximity networks has been tackled
using a variety of technical approaches. Wearable electronic devices, in
particular, have proven to be particularly successful in a variety of settings
relevant for research in social science, complex networks and infectious
diseases dynamics. Each device and technology used for proximity sensing (e.g.,
RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with
specific biases on the close-range relations it records. Hence it is important
to assess which statistical features of the empirical proximity networks are
robust across different measurement techniques, and which modeling frameworks
generalize well across empirical data. Here we compare time-resolved proximity
networks recorded in different experimental settings and show that some
important statistical features are robust across all settings considered. The
observed universality calls for a simplified modeling approach. We show that
one such simple model is indeed able to reproduce the main statistical
distributions characterizing the empirical temporal networks
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Social sensing: Obesity, unhealthy eating and exercise in face-to-face networks
What is the role of face-to-face interactions in the diffusion of health-related behaviors- diet choices, exercise habits, and long-term weight changes? We use co-location and communication sensors in mass-market mobile phones to model the diffusion of health-related behaviors via face-to-face interactions amongst the residents of an undergraduate residence hall during the academic year of 2008--09. The dataset used in this analysis includes bluetooth proximity scans, 802.11 WLAN AP scans, calling and SMS networks and self-reported diet, exercise and weight-related information collected periodically over a nine month period. We find that the health behaviors of participants are correlated with the behaviors of peers that they are exposed to over long durations. Such exposure can be estimated using automatically captured social interactions between individuals. To better understand this adoption mechanism, we contrast the role of exposure to different sub-behaviors, i.e., exposure to peers that are obese, are inactive, have unhealthy dietary habits and those that display similar weight changes in the observation period. These results suggest that it is possible to design self-feedback tools and real-time interventions in the future. In stark contrast to previous work, we find that self-reported friends and social acquaintances do not show similar predictive ability for these social health behaviors.United States. Army Research Office (Award Number FA9550-08-1- 0132)United States. Army Research Laboratory (Cooperative Agreement Number W911NF-09-2-0053)United States. Air Force Office of Scientific Research (Award Number FA9550-10-1-0122
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