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

Facebook

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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