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.

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.

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. This work introduces the "Social fMRI" --- a ubiquitous computing approach that enhances existing methodologies by combining extremely rich data collection in terms of signals, dimensionality, and throughput, together with the ability to conduct targeted experimental interventions with study populations. The proposed research aims to demonstrate the value of the Social fMRI approach via its first instantiation: The Friends and Family living laboratory study, in which a young-family residential community was transformed into a living laboratory for over a year. Using the highly fine-grained and unprecedented longitudinal dataset collected in the study, I aim to show how this approach allows us to gain insights on intricate social mechanisms and interpersonal relationships within the community in ways not possible with traditional approaches. In addition, the mobile-phone based sensing and intervention delivery system developed for this study --- a Social fMRI reference implementation --- is field-proven and robust enough to be reused by other researchers and developers as an open and extensible sensing platform.

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.

In this demonstration we present SnapN'Share, the first sample application of a new platform implementing a wireless, peer-to-peer information system that bypasses the need for any centralized servers, coordination, or administration. SnapN'share runs on wireless pocket devices as well as stationary computers, and enables users to generate and seamlessly share content with different groups and communities that they belong to, as they come into close vicinity of their peers. At CCNC'08 we demonstrate the capabilities and features of the system, as well as its advantages when compared to traditional central-infrastructure based platforms.