Sense and Sensibility in a Pervasive World

Abstract

Abstract. The increasing popularity of location based social services such as Facebook Places, Foursquare and Google Latitude, solicits a new trend in fusing social networking with real world sensing. The availability of a wide range of sensing technologies in our everyday environment presents an opportunity to further enrich social networking systems with fine-grained real-world sensing. However, the introduction of passive sensing into a social networking application disrupts the traditional, user-initiated input to social services, raising both privacy and acceptability concerns. In this work we present an empirical study of the introduction of a sensor-driven social sharing application within the working environment of a research institution. Our study is based on a real deployment of a system that involves location tracking, conversation monitoring, and interaction with physical objects. By utilizing surveys, interviews and experience sampling techniques, we report on our findings regarding privacy and user experience issues, and significant factors that can affect acceptability of such services by the users. Our results suggest that such systems deliver significant value in the form of self reflection and comparison with others, while privacy concerns are raised primarily by the limited control over the way individuals are projected to their peers.

A Survey on Mobile Social Signal Processing

“…[Antoniou et al 2011] deployed Bluetooth dongles inside a building and through Bluetooth discoverable mobile phones they were able to detect if users were in vicinity. CenceMe [Miluzzo et al 2008], Friends and Family [Aharony et al 2011] [Staiano et al 2012] [Singh et al 2013a] [Bauer and Lukowicz 2012] [Efstratiou et al 2012] are other examples of works where they utilised simple Bluetooth discovery to infer if users were in vicinity. Unlike previous approaches, PeopleTones [Li et al 2008] leveraged cell tower readings to estimate if the users are nearby in a larger scale, claiming an error around 322m.…”

The architecture of innovation

“…Other technology used to study face-to-face interactions in the workplace includes wearable cameras, as used in very recent work by Mark et al [15], and mobile phones [8]. While mobile phones are can sense face-to-face interactions less accurately than wearable devices such as the badges we have used in this study, mobile applications to track social interactions have the potential to go further than facilitating the analysis of collected data; they can also feed back the sensed information to users, perhaps with the aim to change their behavior.…”

Tracking serendipitous interactions

“…Recently, research projects have shown that social interactions in the workplace can be detected using mobile phones [4]. Although mobile phones are demonstrably less accurate in detecting face-to-face interactions than specialised wearable devices, these attempts signify an interest in the design of mobile applications that can track social interactions, and deliver tailored services to the end user.…”