Co-Presence Communities: Using Pervasive Computing to Support Weak Social Networks

Abstract

Although the strongest social relationships feature most prominently in our lives, we also maintain a multitude of much weaker connections: the distant colleagues that we share a coffee with in the afternoon; the waitress at a our regular sandwich bar; or the 'familiar stranger' we meet each morning on the way to work. These are all examples of weak relationships which have a strong spatial-temporal component but with few support systems available. This paper explores the idea of 'Co-presence Communities' -a probabilistic definition of groups that are regularly collocated togetherand how they might be used to support weak social networks. An algorithm is presented for mining the Copresence Community definitions from data collected by Bluetooth-enabled mobile phones. Finally, an example application is introduced which utilises these communities for disseminating information.

Recommendation Based on Opportunistic Information Sharing Between Tourists

“…As has been pointed out by Lawrence et al (2006) and supported by our field study, users who consume the same items share similar properties in terms of interest and taste. Our selection of similar users takes advantage of the implication that users who find themselves at the same location because they are consuming the same item tend to be similar.…”

“…We use P N loc,t (u a , u b ) as a condition for the users u a ∈ U and u b ∈ U to be connected by a similarity edge (u a , u b ) ∈ E s in the social graph G s . The resulting social graph corresponds to a copresence community used by Lawrence et al (2006) to disseminate information since spatio-temporal proximity is a necessary and sufficient condition for users to have their devices connected.…”

“…The idea of using physical copresence as a basis for forming social networks has been investigated in a number of projects. For example, AIDE (Ambient Information Dissemination Environment) (Lawrence, Payne, & Roure, 2006) uses ad-hoc Bluetooth connections between mobile devices to share content such as photos, articles, jokes and upcoming events between users. They propose the use of data mining algorithms to analyse encounters and form social networks, introducing the concept of a copresence community as a group of individuals who regularly share the same location at the same time.…”

Breadcrumb SNS: Asynchronous empathy chat for Smart City residents

“…Moreover, it is proved that informal learning, any type of learning that is not done through formal education, is more effective when people are co-located in the same physical spaces compared to virtual spaces [7]. In specific, people give less attention in virtual spaces in the perspective of informal learning compared to physical spaces, providing mere supportiveness to communicate by peripheral awareness that their partner is available for chatting [12]. This means that being in virtual space at the same time is only assisting physical space interaction for the better and more effective informal learning.…”

“…Through this observation result, we can further analyze that all people who visited the place including customers and cafe owner share common thinking and feelings because of their touch point of physical place. As Lawrence, Payne, & Roure [12] presents how co-location's advantage of increasing empathy by enabling simultaneous interaction, the cafe owner will be able to feel empathy to the contents that visitors left because they were co-locating in the same place. Similarly, empathy is created among visitors in Choi-Gun Beer Bar since overall atmosphere, which enables visitors to talk about personal stories, is shared among visitors.…”