RWTH Aachen University

Multidimensional Patterns of Disturbance in Digital Social Networks

Dimitar Denev

Lehrstuhl für Informatik V Information Systems

Prof. Dr. Matthias Jarke

Lehr- und Forschungsgebiet

Knowledge-based Systems

Prof. Gerhard Lakemeyer Ph.D.

Advisors:Ralf KlammaMarc Spaniol

Master Thesis Final Presentation

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Agenda

Motivation

Problem Analysis Approach

State of the Art

Model of Digital Social Networks

Pattern Language

PALADIN

Conclusions and Outlook

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Motivation

Trolls – persons who post only in threads, started by themselves

Context Yahoo! Mailing list „Greek Mythology Link“ Discussion about the movie „Troy“

Message of a troll Troy is a MOVIE – message containing deliberate error Movies are current mythology – message posted as a

generally accepted fact without a proof or analysis Is Christianity and all that other stuff myth, history,

religion or what – inflammatory message including a contemptuous comment on religious thematic.

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Problem Statement

Disturbance as a new source of information and a starting point for learning processes

Hinders the communication in the network Compels individuals to leave the network Difficulties for the disturbances to be discovered or

predicted Multidimensional context of the digital social networks Large size of the networks Knowledge about the disturbances is mostly from

experience and observation

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A pattern language overcomes the difficulties for discovering and describing disturbances

Pattern – a general repeatable solution to a commonly

recurring problem [Alexander 1978]

Machine-readable description of the patterns - XML-

based Pattern Language for Multidimensional

Disturbances

Automatic Analysis of digital social networks for

disturbances with the pattern language

Solution Approach

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Solution Approach

The model of the digital social networks is a based on Actor-Network Theory (ANT) Graph Representation Social Network Analysis (SNA) I* Framework

Multidimensionality of the digital social networks reflected in the model Sociology Computer Science Media Theory Graph Theory Social Capital Theory

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State of the Art

Relations built on the information from Google, Friend-Of-A-Friend network, Bibliography

Dependencies derived from the technical dependencies

Posting in the same thread

Relations

Social Network Analysis, Semantic Web

IndividualsFriend-Of-A-Friend network, Google results

Flink

[Mika 2005]

Temporal Analysis

Developers, Software Components

Eclipse IDE, CVS Repository

Ariadne

[de Souza et al. 2004]

Social Network Analysis, Statistics

Individuals, Mails, Threads, Genres

Mailing ListCOMB [Boudourides et al. 2002]

Analysis Approach

ActorsMedia

Digital Social Networks Projects

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Actor - the basic unit of the model, no difference between technical and social actors.

Semantics, given to the actors from the interpretation in the context of digital social networks: Member – any person or group, part of the digital social

network Medium – an actor which enables the members to

exchange information Artefact – objects created by the members using some

medium Relation – a relation between two actors Network – set of actors along with their relations

Model of Digital Social NetworksActor-Network Theory [Latour 1997]

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Digital Social Network

Digital Media

I* Dependencies

Members

Artefacts

Member Network

Model of Digital Social Networks

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Member types defined according to patterns of

behavior Answering Person Questioner Troll Spammer Conversationalist

Member properties, defined with the help of SNA Centrality types: degree centrality, closeness centrality,

betweenness centrality - determined by the position of the member in the network

Efficiency – describes the existence of structural holes

Model of Digital Social NetworksMembers

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Medium – an actor which enables the members to exchange information Every network supports a set of media A medium affords the creation of a certain set of

artefacts Media types

Email Discussion group Chat room Blog

Wiki

Transaction-based web sites

URL

Model of Digital Social NetworksMedia

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Artefact – objects created by the members using some medium

Artefact types Message Burst Thread Blog entry Comment

Conversation

Feedback (Rating) Artefact properties – author, date of creation, reply to

Model of Digital Social NetworksArtefacts

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I* Dependency types Goal Resource Task Soft goal

Dependencies in digital social networks Structural dependencies

Communication dependency Cross-media dependencies

Coordination dependency Artefact dependency

Model of Digital Social NetworksI* Framework [Yu et al. 1997]

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Network

Coordinator

Gatekeeper

Hub

Member

Iterant Broker

URL

isA

isA

isA

isA

Coordination

Artefact

Communication

Model of Digital Social NetworksI* Dependencies Example

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State of the ArtPattern Languages Projects

„Asynchronous collaborative learning“, „Student group management“

no patterns available

„Working in small groups“, „Overlapping responsibilities“

„Citizen access to simulations“, „Online Community Service Engine“

Pattern Examples

XML SchemaSynopsis, Problem, Context, Forces, Rationale, Pattern Link

Human-Computer Interface

PLML

[Fincher 2004]

Not available

Not available

Not available

Formal Definition

Problem, Analysis, Solution, Context

e-LearningE-LEN

[Steeples et al. 2004]

Essence, Context, Discussion, Implication, Pattern Relations

Computer-Supported Collaborative Work

PoInter

[Viller et al. 2000]

Problem, Context, Discussion, Solution

Social Studies

Public Sphere Project [Schuler 2002]

Pattern StructureDomain

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Pattern – a general repeatable solution to a commonly

recurring problem [Alexander 1978]

Pattern structure

Disturbance

Forces and force relations

Solution

Rationale

Dependencies

Pattern relations

Pattern LanguagePattern Structure

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Variables – simple variables (troll, thread), properties (thread.author) and set variables (v1,…,vn).

Operations Arithmetic (+, -, *, / ) Aggregate (SUM, COUNT, AVERAGE) Logical (&, |, ~, FORALL and EXISTS) Comparison (=, !=, >, <).

Rules for variable binding Simple variables – pattern parameters, actors or set

variables Properties – actor properties or relations Set variables – actors

Interpreted by a finite state automaton

Pattern LanguageFELP

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Troll Pattern: This pattern tries to discover the cases when a troll exists in a digital social network. A troll in the network is considered a disturbance.

Disturbance: (EXISTS [medium | medium.affordance = threadArtefact]) &

(EXISTS [troll |(EXISTS [thread | (thread.author = troll) &

(COUNT [message | (message.author = troll) &

(message.posted = thread)]) > minPosts]) &

(~EXISTS[ thread1, message1| (thread1.author1 != troll) &

(message1.author = troll & message1.posted = thread1 ]))])])

Forces: medium; troll; network; member; thread; message; url

Force Relations: neighbour(troll, member); own thread(troll, thread)

Solution: No attention must be paid to the discussions started by the troll.

Rationale: The troll needs attention to continue its activities. If no attention is paid, he/she will stop participating in the discussions.

Pattern Relations: Associates Spammer pattern.

Pattern LanguageSample Pattern

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Pattern

Disturbance

Variables

Pattern Template

Disturbance

VariablesPattern Parameters

v1,...,vn – variables bound to actors a1,...,an

p1,…, pm – pattern parameters

d – disturbance with d=(v1,...,vn, p1,…, pm).

μ1,…, μm – substitutions for the pattern parameters

Set Pattern Parameters: d = d(v1,...,vn, p1/μ1,…, pm/μm)

Pattern LanguageAlgorithm for Pattern Application

1. Set pattern parameters

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Pattern

Disturbance

Variables

Pattern Template

Disturbance

VariablesPattern Parameters

Pattern Template Instance

Disturbance Instances

Variables Pattern Parameters

Digital Social Network

α1,..., αk – actor instances in the social network

I(ai)=(αi1,…,αir) – instances of

the actor ai

S = (s1,…,st)= I(a1)×…×I(an)

Pattern LanguageAlgorithm for Pattern Application

1. Set pattern parameters

2. Instantiate disturbances

Instantiate disturbances: D = (d(s1),…, d(sp)),

where d(si) = d(v1/α i1,...,vn/αin,p1/μ1,…,pm/μm)

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Pattern

Disturbance

Variables

Pattern Template

Disturbance

VariablesPattern Parameters

Pattern Template Instance

Pattern Instance

Disturbance

Variables Pattern Parameters

Forces ForceRelations

Rationale

Dependencies

Description Solution

Pattern Relations

Disturbance Instances

Variables Pattern Parameters

Digital Social Network

Pattern LanguageAlgorithm for Pattern Application

1. Set pattern parameters

2. Instantiate disturbances

3. Evaluate disturbances

4a. Change Pattern Parameters

4b. Apply Pattern Solution

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ANT Subsystem Web Interface XML Repository

Pattern Subsystem Formal Expression Module XML Pattern Repository Web Interface

Social Network Subsystem Base Social Network Module JUNG Interface IBM DB2 Database

Pattern Application Module Formal Expression Evaluation Pattern Instance Repository

PALADINArchitecture Implementation

PALADIN – PAttern LAnguage for DIsturbances in digital social Networks

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Step 1: define disturbance

expression enter pattern properties

Step 2: bind variables to actors store pattern in the

pattern repository

PALADINWeb Interface

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Troll

Spammers

Members

Size reflects centrality of

the member

Members who participate

in other disturbances,

such as bursts or

structural holes can be

displayed as well

PALADINJUNG Interface Extension

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Case study - 10 patterns of disturbance over 119 social network instances, 17359 individuals, 215 345 mails

PALADINResults

Occurs in big networks where the members are distributed in different clusters.

40No Leader

Occurs for members having neighbours with only one contact.67Structural Hole

Occurs in large networks where disconnected subnetworks exist. Scalability is necessary.

13Independent Discussions

The pattern occurs in the network centered around a member.37Leader

Spammers can be found often in discussion groups. False positives exist.

86Spammer

Troll occurs very rarerly in cultural communities. True negatives exist.

2Troll

Occurs in small networks. The effects of the lack of an answering person must be further checked with content analysis.

61No Answering Person

The existence implies that the network is not popular.67No Questioner

The existence implies little communication in the network.76No Conversationalist

The pattern finds out topics which were very important for certain period of time. Scalability is necessary.

22Burst

RemarksOccurrencesPattern

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Depends on the used media in the network

Relations built on the information from Google, FOAF, Mails, Bibliography

Dependencies derived from the technical dependencies.

Posting in the same thread.

Relations

Social Network Analysis, Semantic Web

IndividualsFriend-Of-A-Friend network, Google results

Flink

[Mika 2005]

Disturbance-oriented, Pattern Repository, Social Network Analysis, Temporal Analysis, Statistics

Media, Members, Artefacts

Any Type of Digital Social Network

PALADIN

Temporal AnalysisDevelopers, Software Components

Eclipse IDE, CVS Repository

Ariadne

[de Souza et al. 2004]

Social Network Analysis, Statistics

Individuals, Mails, Threads, Genres

Mailing ListCOMB

[Boudourides et al. 2002]

Analysis ApproachActorsMedia

Conclusion

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Outlook

Interoperability with applications based on Semantic Web, such as Flink

Methodology for visualization of multidimensional disturbances, must reflect Media Artefacts

SWAP-it [Seeling et al. 2004] InfoSky [Tochterman 2002]

Dependencies

Integration with simulation environment for social networks – can predict disturbances earlier

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THANK YOU FOR YOUR ATTENTION!