Quentin Jones

P3 systems: putting the place back into social networks

The availability of technologies enables a new class of location-aware information systems that link people-to-people-to-geographical-places (P3 systems). P3 systems can strengthen the relationship between social networks and physical places. They can also help individuals leverage location information to make new social ties and coordinate interactions that reinforce existing ties. Using the P3 systems framework, we describe the design space for location-aware community systems and important socio-technical challenges and opportunities.

People-to-People-to-Geographical-Places: The P3 Framework for Location-Based Community Systems

In this paper we examine an emerging class of systems that link People-to-People-to-Geographical-Places; we call these P3-Systems. Through analyzing the literature, we have identified four major P3-System design techniques: People-Centered systems that use either absolute user location (e.g. Active Badge) or user proximity (e.g. Hocman) and Place-Centered systems based on either a representation of people’s use of physical spaces (e.g. ActiveMap) or on a matching virtual space that enables online interaction linked to physical location (e.g. Geonotes). In addition, each feature can be instantiated synchronously or asynchronously. The P3-System framework organizes existing systems into meaningful categories and structures the design space for an interesting new class of potentially context-aware systems. Our discussion of the framework suggests new ways of understanding and addressing the privacy concerns associated with location aware community system and outlines additional socio-technical challenges and opportunities.

A Quantitative Analysis of Power Consumption for Location-Aware Applications on Smart Phones

The industry is producing new wireless mobile devices, such as smart phones, at an ever increasing pace. In terms of processors and memory, these devices are as powerful as the PCs were one decade ago. Therefore, they are perfectly suitable to become the first real-life platforms for ubiquitous computing. For instance, they can be programmed to run location-aware applications that provide people with real-time information relevant to their current places. Deploying such applications in our daily life, however, requires a good understanding of their power requirements in order to ensure that mobile devices can indeed support them. This paper presents a quantitative analysis of power consumption for location-aware applications in our SmartCampus project, which builds a large scale test-bed for mobile social computing. Based on this analysis, we conclude that carefully designed applications can run for up to six hours, while updating the user location frequently enough to support real-time location-aware communication.

The MobiSoC middleware for mobile social computing: challenges, design, and early experiences

Recently, we started to experience a shift from physical communities to virtual communities, which leads to missed social opportunities in our daily routine. For instance, we are not aware of neighbors with common interests or nearby events. Mobile social computing applications (MSCAs) promise to improve social connectivity in physical communities by leveraging information about people, social relationships, and places. This paper presents MobiSoC, a middleware that enables MSCAs development and provides a common platform for capturing, managing, and sharing the social state of physical communities. Additionally, it incorporates algorithms that discover previously unknown emergent geosocial patterns to augment this state. To demonstrate Mo-biSoCs feasibility, we implemented and tested on smart phones two MSCAs for location-based mobile social matching and place-based ad hoc social collaboration.

GDC: Group Discovery Using Co-location Traces

Smart phones can collect and share Bluetooth co-location traces to identify ad hoc or semi-permanent social groups. This information, known to group members but otherwise unavailable, can be leveraged in applications and protocols, such as recommender systems or delay-tolerant forwarding in ad hoc networks, to enhance the user experience. Group discovery using Bluetooth co-location is practical because:(i) Bluetooth is embedded in nearly every phone and has low battery consumption, (ii) the short wireless transmission range can lead to good group identification accuracy, and (iii) privacy-conscious users are more likely to share co-location data than absolute location data. This paper proposes the Group Discovery using Co-location traces (GDC) algorithm, which leverages user meeting frequency and duration to accurately detect groups. GDC is validated on one month of data collected from 141 smart phones carried by students on our campus. Users rated GDC’s groups 30% better than groups discovered using the K-Clique algorithm. Additionally, GDC lends itself more easily to a distributed implementation, which achieves similar results with the centralized version while improving user’s privacy.

Automatic identification of informal social groups and places for geo-social recommendations

Mobile locatable devices can help identify previously unknown ad hoc or semi-permanent groups of people and their meeting places. Newly identified groups or places can be recommended to people to enhance their geo-social experience, while respecting privacy constraints. For instance, new students can learn about popular hangouts on campus or faculty members can learn about groups of students routinely having research discussions. This paper presents a clustering algorithm based on user copresence that identifies such groups and places even when group members participate to only a certain fraction of meetings. Simulation results demonstrate that 90 96% of group members can be identified with negligible false positives when the user meeting attendance is at least 50%. Experimental results using one-month of mobility traces collected from smart phones running Intels PlaceLab location engine successfully identified all groups that met regularly during that period. Additionally, the group places were identified with good accuracy.

Empirical evidence of information overload constraining chat channel community interactions

Prior work has demonstrated that the impact of individual information-processing limits can be observed in dynamics of mass interaction in asynchronous collaborative systems (Usenet newsgroups and email lists). Here we present the first evidence of such impacts on synchronous social interaction environments through the analysis of an Internet Relay Chat network. We highlight how shared public discourse in chat channels appears to be limited to 40 posters in any 20 minute interval, even as the number of channel users increases well into the hundreds. We discuss our findings in terms of understanding the relationship between online community space types and the user interaction dynamics they support.

Seven privacy worries in ubiquitous social computing

Review of the literature suggests seven fundamental privacy challenges in the domain of ubiquitous social computing. To date, most research in this area has focused on the features associated with the revelation of personal location data. However, a more holistic view of privacy concerns that acknowledges these seven risks is required if we are to deploy privacy respecting next generation social computing applications. We highlight the threat associated with user inferences made possible by knowledge of the context and use of social ties. We also describe work in progress to both understand user perceptions and build a privacy sensitive urban enclave social computing system.

Geographic `Place' and `Community Information' Preferences

People dynamically structure social interactions and activities at various locations in their environments in specialized types of places such as the office, home, coffee shop, museum and school. They also imbue various locations with personal meaning, creating group ‘hangouts’ and personally meaningful ‘places’. Mobile location-aware community systems can potentially utilize the existence of such ‘places’ to support the management of social information and interaction. However, acting effectively on this potential requires an understanding of how: (1) places and place-types relate to people’s desire for place-related awareness of and communication with others; and (2) what information people are willing to provide about themselves to enable place-related communication and awareness. We present here the findings from two qualitative studies, a survey of 509 individuals in New York, and a study of how mobility traces can be used to find people’s important places in an exploration of these questions. These studies highlight how people value and are willing to routinely provide information such as ratings, comments, event records relevant to a place, and when appropriate their location to enable services. They also suggest how place and place-type data could be used in conjunction with other information regarding people and places so that systems can be deployed that respect users’ People-to-People-to-Places data sharing preferences. We conclude with a discussion on how ‘place’ data can best be utilized to enable services when the systems in question are supported by a sophisticated computerized user-community social-geographical model.

Making Social Matching Context-Aware: Design Concepts and Open Challenges

Social matching systems recommend people to people. In an ideal world, such systems could be context-aware, in that they would introduce users to each other in situations where they are mutually interested, available and open to meeting (i.e., facilitate a valuable encounter). Unfortunately, todays systems primarily match individuals based on simple similarity and proximity metrics. This paper explores how contextual information available on todays mobile phones could be used to identify opportunities for people to make valuable new connections. Three types of context that are relevant for this work are: relational, social and personal. We present insights gained from several iterations of semi-structured interviewing (N=58) exploring these three types of contexts and propose novel context-aware social matching concepts such as: sociability of others as an indicator of opportune social context; activity involvement as an indicator of opportune personal context; and contextual rarity as an indicator of opportune relational context.