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Defining Situated Social Context for Pervasive Social Computing
Markus Endler1, Alexandre Skyrme1, Daniel Schuster2, Thomas Springer2
1PUC-Rio, Rio de Janeiro, Brazil 2TU Dresden, Dresden, Germany
PerCol 2011
Department of Computer Science Institute for System Architecture, Chair for Computer Networks
Trend for Pervasive Social Computing
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The STIP taxonomy
Applying STIP
Challenges
Summary
Discussion
Outline
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What we are talking about when we talk about: • Pervasive collaboration • Pervasive social networks • Social context
What are the main features of Pervasive Social Computing Systems?
Goal: classify pervasive social systems according the exploited Situated Social Context
Pervasive Social Software
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P3: people-to-people-to-geographical places • Two categories with two subcategories each:
o System focus - People centered: use location information to promote social
interaction - Place centered: link virtual spaces to physical locations
o Communication type - synchronous communication - asynchronous communication
Taxonomy with four dimensions related to interaction • Spatial • Temporal • Inference • People
No continuous dimensions but simple categories
The STIP taxonomy
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What is the spatial scope of the interaction?
S1: Small Scope • People in close proximity or at the same place at different times • People in the same event, in the range of bluetooth • SAMOA – send advertisements to customers in bookshop
S2: Medium Scope • People in the same geo-political region (city, state or country) • FLORA – share local traffic information with people in the same
region
S1+S2: • Dodgeball – send text messages to friends and friends-of-
friends within a „distance of up to ten blocks“ S3: Anywhere
• Place/location has no particular meaning for interaction • Facebook - Connect to people around the globe independent of
current location
S – Spatial Dimension
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What is the duration of the interaction?
T1: Short-term • People can potentially interact spontaneously • Real-time interaction within one session • PeopleTones - notify about friends entering close proximity to
enable spontaneous interactions
T2: Mid-term • People asynchronously interacting over a period of time to
achieve a common goal • PeopleNet – Matching of offers and requests, e.g. for cars
T3: Long-term • People are in a long-term relationship independent from a
particular situation • Facebook – static relationships, ongoing interactions
T – Temporal Dimension
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Are previously unknown interaction partners be detected and how?
I1: Decentralized • Decentralized in a P2P way • No centralized infrastructure and storage of context data • Cluestr – imports profiles of users in social graph of selected
contact, locally analyzes profiles to create groups profile information, e.g. soccer players
I2: Centralized • Inference performed by a central entity • CenceMe – uses social classifiers on central server to propose
new interaction partners based on users activity and location history
I3: None • No inference of social context • Facebook – social links are manually created
I – Inference Dimension
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What is the granularity of interactors in the system?
P1: Individuals • Interaction is performed between individual users like friends,
collegues, friends-of-friends • PeopleTones – proximity detection and interaction is
performed between individual users
P2: Groups • Social context consists of one or multiple groups • Group is a cluster of friends, a group of people at the same
place, etc. • Cluestr – system creates groups of users with similar attributes
to send messages to group
P3: Anonymous Community • People interact with unknown set of persions visible as a
community, e.g. using the same application • Twitter – tweed messages to an anonymous community
P – People Dimension
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Characterizing Pervasive Social Systems with STIP
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Pervasive Social Systems are sytems that • operate within small or medium location scope (S1 or S2) • supports short-term or mid-term interactions (T1 or T2) • dynamically inferred social context (I1 or I2) • enables interactions between individuals or groups (P1 or P2)
Each dimension has an exclusion criteria (*3)
Dimension/ category
1 2 3
(S)patial Small scope Medium scope Anywhere
(T)emporal Short-term Mid-term Long-term
(I)nference Decentralized Centralized None
(P)eople Individuals Groups Anonymous Community
Online social network Provides web-client and mobile clients
S3: you can connect to people all over the world, location has no particular meaning for linking or interactions
S1/T2: for Facebook Places T1/T2: supports immediate interactions via chatting and
asynchronous interactions using comments, wall etc. I3: friends-of-friends are suggested as new connections
but no matching of profiles P1: operates at the granularity of individual users
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A middleware system for grouping potential communication partners to social networks based on • Physical proximity • Common attributes in user profiles and place attributes
Use case: • Bookshop forwards advertisement messages to customers
currently in the shop or were in the past
S1: fosters interaction of people in close proximity T1: immediate forwarding of advertisment messages
T2: also to people who entered the shop in the past I2: central rules for matching are applied P1: at granularity of individual users
SAMOA
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S2: data gathered locally, potentially data and information can be accessed from anywhere (mid-range since interest in content and info. might decrease with increased distance)
T1: immediate sharing of information and data I1: inference of context is done in a local, decentralized way P2: groups of people are created for data collection at
locations (sharing of information and content is between a service provider and individual users)
FLORA
Framework for location-specific real-time applications • Users act as sensors
at their current users position
• Users on other positions can access that information
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S3: not location-dependent T1/T2: calls and text messages I1: server performs inference and context
propagation P1: granularity of individual users
Friendlee
System for analyzing users call and messaging history to identify most meaningful ties
Ambient awareness of intimate network • sharing of context at different
granularities • browsing of connections of close
contacts • search their social network for
people and businesses.
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S1: interact with people in close proximity T1/T2: short or mid-term interactions between matching
users (mainly in physical world) I2: data processing on server-side P1: operates on individual users (also supports notion of
groups)
MobiSoC
Middleware for capturing, managing, and sharing the social state of physical communities • analyzing user profiles and
mobility traces, also place profiles
Use case • Compute affinity level between
pairs of users to meet at the campus during break
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System for the detection and notification of buddy proximity on mobile phones
Use Case: • Have a cup of coffee with a friend
if he is nearby
PeopleTones
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S1: fosters interaction of people in close proximity T1: spontaneous interactions are supported I2: centralized architecture for location sharing and
proximity detection P1: operates at the granularity of individual users
A framework for building social-networking-enabled context-aware services
Use Case • Detects the Facebook ID of each nearby user and obtains
information about the users‘ music preferences • Plays music that reflects the preferences of one or more
users residing in a common physical space
S1: exploits proximity to match music preferences T1: music is played immediately I2: matching of music preferences is done on a server P2: handles user in proximity as user group
Socialaware
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System summary
System Spatial Temporal Inference People
FLORA S2 T1 I1 P2
Socialaware S1 T1 I2 P2
Cluestr S3 T3 I1 P2
SAMOA S1 T1 I2 P1
VENETA S1 T1 I1 P1
Dodgeball S1+S2 T1 I2 P1
CenceMe S1+S2 T1+T2 I2 P1
PeopleNet S2 T2 I1 P1
PeopleTones S1 T1 I2 P1
MOSS S3 T2 I2 P2
Friendlee S3 T1/T2 I2 P1
MobiSoC S1 T1/T2 I2 P1
MobiClique S1 T1 I3 P2
Google Latitude S3 T3 I3 P1
Facebook (Places) S3 (S1) T1/T2 I3 P1
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Lack of standards • Interoperability is a key feature for making social networks
pervasive • Currently no standards for data representation, protocols and
interfaces • Semantically mapping/correlating of information
Social data mining, inference and matchmaking • High potential to extract more social context • Efficient algorithms have to be developed to correlate linked
users information • Extract implicit information from social relations
Dealing with uncertainty • Advances in Pervasive Social Computing heavily rely on
inference of context information • Introduced uncertainty critical for user adoption
Privacy Issues • User awareness of issues • Let the user control its private data
Challenges for Pervasive Social Computing
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Increasing number of systems exploit Situated Social Context • Creation of new social ties based on location and
proximity • New forms of interaction in location-based social
networks
Introduced STIP taxonomy can help to analyse systems according the usage of Situated Social Context
Classification of systems according to STIP helped to identify challenges for Pervasive Social Systems
Summary
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Dimensions appropriate?, Further dimensions?
Categories appropriate?
Visual representation of STIP
Discussion
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Dimension/ category
1 2 3
(S)patial Small scope Medium scope Anywhere
(T)emporal Short-term Mid-term Long-term
(I)nference Decentralized Centralized None
(P)eople Individuals Groups Anonymous Community