Sharing our digital aura through social and physical proximity

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.Includes bibliographical references (p. 153-160).People are quite good at establishing a social style and using it in different communications contexts, but they do less well when the communication is mediated by computer networks. It is hard to control what information is revealed and how one's digital persona will be presented or interpreted. In this thesis, we ameliorate this problem by creating a "Virtual Private Milieu", a "VPM", that allows networked devices to act on our behalf and project a "digital aura" to other people and devices around us in a manner analogous to the way humans naturally interact with one another. The dynamic aggregation of the different auras and facets that the devices expose to one another creates social spheres of interaction between sets of active devices, and consequently between people. We focus on the subset of networking that deals with proximate communication, which we dub Face-to-Face Networking (FtFN). Network interaction in this space is often analogous to human face-to-face interaction, and increasingly, our devices are being used in local situations. We describe a VPM framework, key features of which include the incorporation of trust and context parameters into the discovery and communication process, incorporation of multiple contextunique identities, and also the support for multiple degrees of security and privacy. We also present the "Social Dashboard", a readily usable control for one's aura. Finally, we review "Comm.unity", a software package that allows developers and researchers easy implementation and deployment of local and distant social applications, and present two applications developed over this platform.Nadav Aharony.S.M

Measuring and designing social mechanisms using mobile phones

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-168).A key challenge of data-driven social science is the gathering of high quality multi-dimensional datasets. A second challenge relates to the design and execution of social experiments in the real world that are as reliable as those within a controlled laboratory, yet yield more practical results. We introduce the Social Functional Mechanism-design and Relationship Imaging, or "SocialfMRI" - an approach that enhances existing computational social science methodologies by bridging rich data collection strategies with experimental interventions. In this thesis, we demonstrate the value of the Social fMRI approach in our Friends and Family study. We transformed a young-family residential community into a living laboratory for 15 months, through a very fine-grained and longitudinal data collection process combined with targeted experimental interventions. Through the derived dataset of unprecedented quality, the Social fMRI approach allows us to gain insights into intricate social mechanisms and interpersonal relationships within the community in ways not previously possible. This thesis delivers the following contributions: (1) A methodology combining a rich-data experimental approach together with carefully designed interventions, (2) a system supporting the methodology - implemented, field-tested, and released to the world as an open-source framework with a growing community of users, (3) a dataset collected using the system, comprising what is, to date, the richest real-world dataset of its genre, (4) a very large set of experimental findings that contribute to our understanding of important research questions in computational social science in addition to demonstrating the methodology's potential. Among the results described in this thesis are the design and evaluation of a novel mechanism for social support in a health-related context, the observation that the diffusion of mobile applications relies more on the face-to-face interaction ties than on self-perceived friendship ties, and a gained understanding of the evolution of modeling and prediction processes over time and varying sample sizes.by Nadav Aharony.Ph.D

Composite Social Network for Predicting Mobile Apps Installation

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)

The Social fMRI: Measuring, Understanding, and Designing Social Mechanisms in the Real World

A key challenge of data-driven social science is the gathering of high quality multi-dimensional datasets. A second challenge relates to design and execution of structured experimental interventions in-situ, in a way comparable to the reliability and intentionality of ex-situ laboratory experiments. In this paper we introduce the Friends and Family study, in which a young-family residential community is transformed into a living laboratory. We employ a ubiquitous computing approach that combines extremely rich data collection in terms of signals, dimensionality, and throughput, together with the ability to conduct targeted experimental interventions with study populations. We present our mobile-phone-based social and behavioral sensing system, which has been deployed for over a year now. Finally, we describe a novel tailored intervention aimed at increasing physical activity in the subject population. Results demonstrate the value of social factors for motivation and adherence, and allow us to quantify the contribution of different incentive mechanisms.U.S. Army Research Laboratory (Cooperative Agreement Number W911NF-09-2-0053)United States. Air Force Office of Scientific Research (Award FA9550-10-1-0122

How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage

As truly ubiquitous wearable computers, 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, and so on. In many cases, this analysis work is the result of exploratory forays and trial-and-error. In this work we investigate the properties of learning and inferences of real world data collected via mobile phones for different sizes of analyzed networks. In particular, we examine how the ability to predict individual features and social links is incrementally enhanced with the accumulation of additional data. To accomplish this, we use the Friends and Family dataset, which contains rich data signals gathered from the smartphones of 130 adult members of a young-family residential community over the course of a year and consequently has become one of the most comprehensive mobile phone datasets gathered in academia to date. Our results show that features such as ethnicity, age and marital status can be detected by analyzing social and behavioral signals. We then investigate how the prediction accuracy is increased when the users sample set grows. Finally, we propose a method for advanced prediction of the maximal learning accuracy possible for the learning task at hand, based on an initial set of measurements. These predictions have practical implications, such as influencing the design of mobile data collection campaigns or evaluating analysis strategies

Friends don't lie: inferring personality traits from social network structure

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.European Commission (PERSI Project within the Marie Curie COFUND-FP7)Italy. Ministero dell'istruzione, dell'università e della ricerca (FIRB S-PATTERNS project

Supertube domain-walls and elimination of closed time-like curves in string theory

We show that some novel physics of supertubes removes closed time-like curves from many supersymmetric spaces which naively suffer from this problem. The main claim is that supertubes naturally form domain-walls, so while analytical continuation of the metric would lead to closed time-like curves, across the domain-wall the metric is non-differentiable, and the closed time-like curves are eliminated. In the examples we study the metric inside the domain-wall is always of the G\"odel type, while outside the shell it looks like a localized rotating object, often a rotating black hole. Thus this mechanism prevents the appearance of closed time-like curves behind the horizons of certain rotating black holes.Comment: 22 pages, JHEP3 class. V2: Some corrections and clariffications, references added. V3: more corrections to formulas, results unchanged. V4: minor typos, as published in PR

Nonperturbative aspects of ABJM theory

Using the matrix model which calculates the exact free energy of ABJM theory on S^3 we study non-perturbative effects in the large N expansion of this model, i.e., in the genus expansion of type IIA string theory on AdS4xCP^3. We propose a general prescription to extract spacetime instanton actions from general matrix models, in terms of period integrals of the spectral curve, and we use it to determine them explicitly in the ABJM matrix model, as exact functions of the 't Hooft coupling. We confirm numerically that these instantons control the asymptotic growth of the genus expansion. Furthermore, we find that the dominant instanton action at strong coupling determined in this way exactly matches the action of an Euclidean D2-brane instanton wrapping RP^3.Comment: 26 pages, 14 figures. v2: small corrections, final version published in JHE