[IEEE 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) - Seattle, WA, USA (2011.03.21-2011.03.25)] 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops) - Defining Situated Social Context for pervasive social computing

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  • Defining Situated Social Context for Pervasive Social Computing

    Markus Endler, Alexandre SkyrmeLaboratory for Advanced Collaboration

    PUC-RioRio de Janeiro, Brazil

    endler@inf.puc-rio.br, askyrme@inf.puc-rio.br

    Daniel Schuster, Thomas SpringerComputer Networks Group

    TU DresdenDresden, Germany

    daniel.schuster@tu-dresden.de, thomas.springer@tu-dresden.de

    AbstractThere is a multitude of systems in the area of mo-bile and pervasive social computing providing and using socialcontext for pervasive interaction between persons. Currently,it is hard to distinguish the different approaches as there is noappropriate taxonomy available yet. In this paper we introducethe STIP taxonomy to define social context in the area of socialcomputing and further propose to use the term Situated SocialContext for a more focused subset of social context for pervasivesocial computing. We elaborate the taxonomy with a smallsurvey of mobile social networking systems and discuss futureresearch challenges.

    Keywords-social networks; mobile computing; pervasive col-laboration; taxonomy; survey; social context

    I. INTRODUCTION

    The advent of social networking applications, and espe-cially of websites dedicated to promoting social interaction,caused a social phenomenon in the Internet. Millions ofusers, some of who have little or no prior experienceusing online applications, regularly turn to websites suchas Facebook, Twitter or Orkut, just to name a few, tokeep in touch with friends, join common interest groupsand publish thoughts, opinions, recommendations and upto date information about themselves. As more and morepeople join and use such social networking websites, virtualcommunities are fostered and online social interaction rises.

    Meanwhile, the ever growing popularization of mobiledevices with increasing features, especially smartphones,caused a comparable phenomenon in the physical world.As pervasive computing steadily becomes a reality throughcontext-aware applications [1] which help to blur the linebetween the physical and the virtual worlds, people findthemselves increasingly connected, even when on the move.

    As both worlds are growing together with applicationslike Foursquare, Gowalla or Facebook Places, online socialcommunities become pervasively accessible providing aplatform for manifold interaction with network contacts bothin real-time and non real-time. Thus, social computing isgetting mobile or even pervasive, therefore creating a trendof Pervasive Social Computing.

    However, in order to build social applications that benefitfrom mobile technologies and location-awareness, it is fun-damental to understand what type of social information can

    be explored in a mobile environment, how it can be gatheredand how it can be composed into a Social Context whichcan then be used by applications to enhance users socialexperience.

    The term Social Context can have many meanings ordefinitions, but most of them focus on the possible formsof relationships and interactions among people. Since inthis paper we are specifically interested in social contextthat enables location-based, spontaneous interaction amongpeople, we use the term Situated Social Context which wedefine as follows:

    Situated Social Context of an individual is the set of peo-ple that share some common spatio-temporal relationshipwith the individual, which turn them into potential peers forinformation sharing or interaction in a specific situation.

    This paper explores the meaning of situated social contextfor mobile users. Its remainder is organized as follows.Section II proposes and briefly explains dimensions ofsituated social context which are then applied in Section IIIpresenting an overview of some related work. Section IVdiscusses some key challenges concerning situated socialcontext. Finally, Section V presents some conclusions.

    II. SOCIAL CONTEXT TAXONOMY

    In order to account for the many possible meanings anddefinitions of Social Context [2], [3], and to clearly explainwhat we mean by Situated Social Context, we elaboratedthe following four-dimensional Social Context definitionspace, where each specific definition of Social Context ischaracterized by its spatial, temporal, inference and tar-get peoples characteristics. We call it the Social ContextSTIP-taxonomy. Of course, for most of the dimensionsit would be more accurate to represent a continuum ofalternatives, but for the sake of tangibility and comprehen-sibility we decided to stipulate only some coarse-grainedalternatives/options/intervals.

    Our work is not the first attempt to classify systems thatcombine location and social information to support newmeans of user interactions. In [4] the authors propose anddescribe a framework for classifying people-to-people-to-geographical places (P3) systems into four general cate-gories, considering if they are synchronous or asynchronous,

    Second IEEE Workshop on Pervasive Collaboration and Social Networking

    978-1-61284-937-9/11/$26.00 2011 IEEE 519

  • people- or place-centered. The main distinctive feature of ourtaxonomy is that it also classifies systems according to thegranularity in spatial, temporal and people dimensions, aswell as their capabilities of inferring new social correlations.

    S - Spatial Dimension

    This dimension determines to which extent the geographi-cal distance among the peers is relevant for the establishmentof the social links and interactions.

    S1: Small scope:This mode of social context comprehends only co-located/nearby people, i.e. people that have thepotential to interact in direct, face-to-face modewithin close proximity. For example this is thecase for people participating in the same event likea conference or experiencing a common situationsuch as a car accident at some street. A small scopeis also given for people being at the same place atdifferent points in time interacting via that place.

    S2: Medium scope:This mode encompasses people located in a samegeo-political place or region. For example, allcitizens of a city, region or country.

    S3: Anywhere:In this mode, the current place/location of the peersis of no importance to establish interaction.

    T - Temporal Dimension

    This dimension determines the temporal aspect of thesocial interactions, in terms of peer discoverability andmaintenance of the social links or interactions, which aredirectly related to the granularity of the user activity, roleand group membership.

    T1: Short-term activity:This mode includes all people that meet (or havethe potential of meeting) during a short period oftime (e.g., while waiting at the bus stop, by bump-ing into each other on the street, or being online atthe same time in the same social application etc.).

    T2: Mid-term activity:This includes individuals which may interact forsome time because of their mid-term goals, forexample helping each other to solve a problem athand, e.g. finding a rare music title, installing apiece of software, fixing a flat car tire, etc.

    T3: Long-term activity:This mode includes people that have a direct orindirect relationship due to their long-term activity,role or participation in a common project or com-mon interest. Examples include families, fellowsand friends, club members, employees, etc.

    I - Inference Dimension

    This dimension describes the mechanism (if any) usedto infer social context. Many systems deploy some sort ofdetection of social correlation between individuals or groups.

    I1: Decentralized:Social matching or inference is decentralized in aP2P way. This has the advantage that no expensiveserver infrastructure is needed but requires com-plex mechanisms to route and process information.

    I2: Centralized:Inference is done at a central entity. Users sendthe information needed to infer social context toa server which processes and sends inferred socialcontext information back to the mobile devices.

    I3: None:No inference of social context is provided.

    P - People Dimension

    The people dimension represents granularity of peopleforming the social context.

    P1: Individuals:The social context of a person consists of indi-vidual persons (i.e., friends, friends-of-friends orunknown individuals) he/she wants to interact with.

    P2: Groups:The social context of a person consists of a groupor multiple groups of people he/she wants to in-teract with. A group can be a cluster of friendsfrom the social network, a group gathering at someplace, or the like.

    P3: Anonymous Community:The social context of a person consists of an anony-mous community with the same social application.

    The STIP-taxonomy, summarized in table I, helps usto more precisely define Situated Social Context. Thisterm can be used for any system that provides and usesinferred (I1 and I2) social context to enable interaction withindividuals or groups (P1 and P2), in small or medium scoperegarding location of participants (S1 and S2) within short-term or mid-term activities (T1 and T2).

    Dimensions 1 2 3(S)ocial Small Medium Anywhere

    scope scope(T)emporal Short-term Mid-term Long-term

    activity activity activity(I)nference Decentralized Centralized None

    (P)eople Individuals Groups AnonymousCommunity

    Table ITHE STIP-TAXONOMY SUMMARIZED.

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  • Using this definition we can further classify existingsystems in the area of mobile social software along thefour dimensions. As each dimension has an exclusion cri-teria (*3), we can also specify which of the mobile socialapplications actually do not provide or use situated socialcontext but rather some more general type of social contextto foster interaction between users.

    III. SELECTED WORKS

    To more clearly describe the usefulness of the STIP-taxonomy, we present a short survey of mobile social soft-ware ordered according to the four introduced dimensions.

    A. Spatial Dimension

    In this dimension, research works can be categorizedinto the ones that support only small scope (S1) proximitydetection, the ones that focus exclusively on medium scope(S2), and some works that support both small- and medium-scope co-location detection.

    In the first category, some of the most prominent sys-tems/approaches are: SAMOA [5] is a middleware sys-tem enabling inference of previously unknown social pat-terns by some semantic analysis that tries to match ac-tivities/attributes of users and places profiles. The goal isto determine potential peers for social interaction basedon these profiles and physical small scope co-location.MobiClique [6] is another interesting system in this cate-gory. It employs a decentralized proximity detection, wheregeographical and social context is associated by means ofa social networking service based on a store-carry-forwardcontent dissemination. A similar decentralized approach isalso adopted by VENETA [7]. It is a mobile social networkplatform able to explore the social neighborhood of a user bydetecting common friends of friends which are in the userscurrent physical proximity. This is done by Bluetooth-basedproximity detection and by comparing their phone contactentries. Similarly, WhozThat [8] also uses phone proximityto exchange social network (e.g. Facebook) IDs, which arethen used to fetch personal profiles from the social network.

    Among the systems of the second category, S2, one shouldmention PeopleNet [9], which is a P2P architecture forinformation search in a distributed manner by propagatingqueries of a given type via peer-to-peer connectivity to usersspecific geographic locations, named bazaars. Because abazaar can span a quite large geographical region that isdetermined by the set of all directly or indirectly reach-able devices, PeopleNets notion of co-loction is clearlyof medium scope (S2). Another interesting work in thiscategory is MobiSoc [10], which is able to infer previouslyunknown social patterns by analyzing People profiles andtheir mobility traces, Place profiles, and by employingPeople-People and People-Place Affinity Learning methods.Hence, geographical and social contexts are associated todiscover new potential social ties between users. Co-location

    of users is verified on the server-side, rather than employingdirect proximity detection. Since the goal is to discover newsocial links based on the set of the users commonly visitedplaces, rather than particular places for a here and nowpeople discovery, the spatial dimension is S2. A similarspatial scope is also considered in the work on Mobile SocialEcosystems [11].

    Among the analyzed works on Situated Social Context afew ones have a rather flexible definition of co-location, andthus span the S1 and S2 modes. One of them is Dodge-ball [12], that was probably the first application combiningLBS (Location-based Service) and Social Networks. Dodge-balls goal was to share the users location (as a symbolicplace name) in the social network and to send text messagesto friends and friends-of-friends within a distance of up toten-blocks. Therefore, Dodgeball could be used both forsmall scope and medium-scope interactions, depending onthe size of the coverage set by the user. The other work witha spatially-flexible approach is CenceMe [13]. CenceMe isa commercial social network for the iPhone/iPod Touch thatis able to collect, classify and infer users present statusand activity from the mobile devices sensors and exportthis information, in real-time, into social networks. It alsohas a Social Context classifier running at a central backendserver that computes a users neighborhood condition, i.e.the CenceMe buddies in a users surrounding area. Sinceit is aimed both at detecting new social ties based onsimilar visited places (like in [10]) and at identifying userproximity, it supports modes S1 and S2.

    B. Temporal Dimension

    Regarding the temporal dimension, many of the researchsystems we reviewed fall in category T1, i.e., supportingshort-term activities. A typical example can be seen inPeopleTones [14], where users of the service get an alertwhenever a friend or buddy is in close proximity. The goal issimply to be informed about such a nice to know situationand to be able to contact a person directly, enabling somekind of spontaneous interaction not possible before (e.g., tohave a cup of coffee together right now). As the goal is tosupport spontaneous activities whenever there is time for it,the alert can be unobstrusive (e.g., by phone vibration) thusnot requiring the user to look all the time at the phone, norto be interrupted by an alert.

    Most of the systems with proximity detection can beclassified in S1 and T1, as proximity is most often used toinvolve any here and now activity which is made possibleby the proximity situation. So many of the systems alreadyintroduced for the spatial dimension like MobiClique [6],SAMOA [5] and VENETA [7] fall in category T1.

    FLORA [15] is an example of T1 systems beyond prox-imity, where users collect information for real-time collabo-ration. One possible application scenario is the collection oftraffic updates to alert users entering a region with a traffic

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  • jam. Further scenarios include measuring people densityat public places, tracking...

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