Nadav Aharony

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.

Using Social Sensing to Understand the Links between Sleep, Mood, and Sociability

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 smart phone 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.

Stealing Reality: When Criminals Become Data Scientists (or Vice Versa)

Stealing-reality attacks attempt to steal social network and behavioral information through data collection and inference techniques, making them more dangerous than other types of identity theft.

Incremental Learning with Accuracy Prediction of Social and Individual Properties from Mobile-Phone Data

As truly ubiquitous wearable computers, mobile phones are quickly becoming the primary source for social, behavioral, and environmental sensing and data collection. Todays smart phones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals regarding the phone, its user, and their environment. A great deal of research effort in academia and industry is put into mining this 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. Adding to the challenge, the devices themselves are limited platforms, hence data collection campaign must be carefully designed in order to collect the signals in the appropriate frequency, avoiding the exhausting the the devices limited battery and processing power. Currently however, there is no structured methodology for the design of mobile data collection and analysis initiatives. In this work we investigate the properties of learning and inference of real world data collected via mobile phones over time. In particular, we analyze how the ability to predict individual parameters and social links is incrementally enhanced with the accumulation of additional data. To do so we use the Friends and Family dataset, containing rich data signals gathered from the smart phones of 140 adult members of an MIT based 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 for predicting social and individual properties from sensed mobile phone data over time, including detection of life-partners, ethnicity, and whether a person is a student or not. Finally, we propose a method for predicting the maximal learning accuracy possible for the learning task at hand, based on an initial set of measurements. This has various practical implications, such as better design of mobile data collection campaigns, or evaluating of planned analysis strategies.

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. Todays 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.

Sleep, mood and sociability in a healthy population

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 spouses sleep and mood may have an effect on the subjects 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.

Security and Privacy in Social Networks

Security and Privacy in Social Networks brings to the forefront innovative approaches for analyzing and enhancing the security and privacy dimensions in online social networks, and is the first comprehensive attempt dedicated entirely to this field. In order to facilitate the transition of such methods from theory to mechanisms designed and deployed in existing online social networking services, the book aspires to create a common language between the researchers and practitioners of this new area- spanning from the theory of computational social sciences to conventional security and network engineering.

Social Area Networks: Data Networking of the People, by the People, for the People

In this position paper we explore a holistic approach for the integration of social and human-level concepts with all layers of the communication network. This integration is bidirectional - the social information can help inform and configure network-level parameters, while network information can contribute to the gathering and learning of social-level information. We review existing work and emerging trends in the area, as well as propose new ideas for socially-aware network applications. We present the motivations for a unified approach for integrating socially related information throughout the networking stack, and introduce the concept of a Social Area Network (SocAN), which encompasses network architectures built for and around people and their social relationships.