modeling communities in information systems: informal learning communities in social media
TRANSCRIPT
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
1/36
TeLLNet
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
M. Sc. Zinayida Kensche (nΓ©e Petrushyna)
Doctoral Thesis Defense
Chair of Information Systems and Databases
RWTH Aachen University
Aachen
November 17, 2015
Modeling Communities in Information Systems: Informal Learning Communities in Social Media
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
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TeLLNet Outline
Motivation and Research Questions
Background and Context of Informal Learning
Continuous Support of Community Life Cycle
Test cases
β Modeling Informal Learning Communities in
Learning Forums
β Competence Management in Lifelong Learning Communities
Conclusion and Outlook
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TeLLNet
Formal learning communities are students in lectures
Informal learning communities are self-organized
Stakeholders care about their communities:
β What are insights of informal learning communities?
β Their success and failures?
β Can communities learn from other communities?
β How do communities evolve?
Motivation
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet Social Media Usage for Informal Learning
Learning Analytics Conceptual Modeling
Formal learning: a MOOC
Informal learning:forums, blogs,mailing lists, chats, social network sites
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet Research Questions
Connecting advanced computer science tools and learning theories β
the interdisciplinary character of the work Suh & Lee, 2006, Kleanthous & Dimitrova, 2007, 2010, Abel et al., 2011
Creating stereotype models and selecting suitable ones that describe community situations, needs, types, and future positions
Zhang & Taniru, 2005, Li et al. 2008, Hilts & Yu, 2011, Fereira & Silva, 2012
Advanced computer science tools support communities by providing results of analytical investigation and estimation of community needs
Wolpers et al., 2007, Kodinger et al., 2008, Upton & Kay, 2009, Dascalu et al., 2010,
Scheffel et al. 2011, Karam et al., 2012, Verbert et al., 2012, Rabbany k. et al., 2012
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
Networked learning & community of practice: learning in collaboration Wenger, 1998, Dillenbourg, 1999, Stahl, 2006
Learning Theories Recapitulation
1934 1954 19711972 1973 1980 1986 1998
Social constructivism: social influence on learning Vygotsky, 1934/1986
Social learning/cognitive theory: society is pivotal for a learner Bandura, 1971, 1986
1999 2006
Cognitivism: individual style of learning Pask and Scott, 1972
Behaviorism: learning processes are guided interactions are shaped, Skinner, 1954
Cognitive constructivism: learning by discovery Piaget, 1973, Papert, 1980
Teaching machine
Lack of social aspects of learning
Cognitive processes
Assimilating new and existing knowledge
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet Community of Practice and Technology
Digital Media/
Community
Information Systems
Web 2.0 Processes/
i* Models/ Strategies(Cross-media Analysis)
Members(Social Network Analysis,
Community Detection &
Evolution)
Network of Artifacts(Emotional Analysis, Intent Analysis,
Information Retrieval. Social Network
Analysis)
Network of Members
Communities of practice
Media Networks
Communities of Practice: collaborating, sharing same goals and interestsWenger, 1998
Data management Klamma, 2010
Community analytics Yu, 2009
Conceptual modeling Klamma, 2013
correspond to CoP dimensions andactors in media networks
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet Overview of Research Answers
Systematic workflow for overall approach Petrushyna et al., 2014
Ground laying model for informal learning communities in digital media Petrushyna et al., 2010
Repository of model stereotypes Petrushyna et al., 2014
Simulation approach for refining online informal learning community models
Tool set for modeling, monitoring and analyzing of informal learning communities in social media Petrushyna & Klamma, 2008, Klamma & Petrushyna, 2010, Krenge et al., 2011, Song et al., 2011, Petrushyna et al., 2014, Petrushyna et al., 2014a, Petrushyna et al., 2015
RQ1
RQ2
RQ2
RQ2
RQ3
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet Technical Contributions
The metamodel of informal learning communities in digital media The i*-REST service for modeling communities in i* Petrushyna et al., 2014
Professional and social competence modeling using social networkanalysis Song et al., 2011
The general agent-based model of informal learning communities Community stereotype model repository Petrushyna et al., 2014
Mapping of i* models to Java based agents Simulations of agent-based models of learning communities
A design of data cube appropriate for heterogenous data storage and rapid query processing Klamma and Petrushyna, 2008
The TargETLy service for community analysis Petrushyna et al., 2015,
Krenge et al., 2011, Petrushyna et al., 2011
Implementation of community detection/evolution algorithms for large networks in distributive environment
The competence management support framework for lifelong learning communities Song et al., 2011
Estimation of learning quality using community analysis Pham et al., 2012
Modeling
Refinement
Monitoring
Analysis
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet Workflow of Community Learning Analytics
Co
nti
nu
ou
s re
qu
irem
en
ts
Maintenance of stored community digital traces
Defining user patterns, emotions, intents, concepts and topics of interest
Detecting communities and their evolution
Communities are represented by stereotype modelsSmith and Kollock, 1999, Cheung et al., 2005, Madanmohan and Siddhesh, 2004,Niegemann and Domagk , 2005, Fisher et al., 2006, Turner et al., 2005
Models reveal community requirements and insights
Stakeholders maintain communities operating suitable models
Simulations used to identify possible community changes
Jarke et al., 2008
Petrushyna et al., 2014 RQ1
Modeling
Refinement
Monitoring
Analysis
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
Learningresource
Learning goal
Acceptance
Support learning process
Learner A Expert
Community Learner
Modeling: i* Modeling Approach for Informal Learning Community Modeling
RQ2
Dependency resource
Goal
Softgoal
Task
Agent Role
Depender Agent
Dependee Agent
+ models can beextended to describethe rationale of agents
+ point out dependencies betweenhuman and non-human agents
+ emphasize agents, their types and roles
+ indicate intentions in social networks
+ models can be createdusing XML-based format
- too abstract
- before applying i* modeling training is required
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet Modeling: A General Learning Community Model
RQ2
Learner
Community
Learner A
composes
interacts
Learner B
creates
space for knowledge
sharing
rules and
policies
limitations
learns from
Resource dependency
Agent
Dependee
Depender
Task dependency
Agent
Goaldependency
Mutual engagement Shared repertoire Joint enterprises
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
ProcessProcess
ArtifactArtifact
initializes
D
D
MediumMedium hostsD D
consists of DD
influences
D
D
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TeLLNet
Resource dependency
Agent
Dependee
Depender
Task dependency
Agent
Goaldependency
Stereotypes of Learning Communities
Communities can be represented by stereotype models Smith and Kollock, 1999, Madanmohan and Siddhesh, 2004, Cheung et al., 2005, Niegemann and Domagk , 2005, Turner et al., 2005, Fisher et al., 2006
RQ2
Teacher-oriented Learner-oriented Lifelong learners-oriented
Question-answer Dispute Innovative
Culture-sensitive At workplace Community of interest
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
Refinement: A General Agent-based Model ofAn Informal Learning Community in Media
Society πππ = π΄, π΄ππ‘π΄ = {π΄1β¦ π΄π} is a set of agents
π΄ππ‘ is a set of predefined actions of agents π΄
π΄ππ‘πππππ‘π π‘ = π΄ππ‘πππππ‘1β¦π΄ππ‘πππππ‘ππ‘ are created by agents A
with π΄ππ‘ at π‘
π π‘ β π΄ Γ π΄ Γ β+ are social relations, where π‘ is a time point
π΄π(π‘)
πΆπ‘, where πΆπ‘ = πΆ1β¦ πΆππ‘β πΆ , πΆπ‘ is a set of communities
πππππ = {πππππ’π1, β¦ πππππ’ππ}, where
π΄ππ‘πππππ‘π π‘π(π‘)
πππππ’ππ
π = π1β¦ ππ is a set of strategies of agents, where S = d β Ξπ = π πππππππππ‘π¦, πππππππππ‘πππ ππ‘π‘ππβππππ‘
Connecting with known agents Rich get richer
Not a Web 2.0 Web 2.0Barabasi & Albert, 1999RQ2
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet Monitoring: Mediabase Cube
Mediabase Cube includes all actors of a learning community in dimensions + additional Time dimension
Results of analysis are stored in Facts tablesRQ2
Klamma, 2010
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet Analysis Workflow
interactions of learners Graph-based analysis
Services responsible for mutual engagement dimension
Services responsible for joint enterprises and shared repertoire dimensions
texts of communities Language-based analysis
Social Network Analysis
Community Detection & Evolution
Emotional Analysis
Intent Analysis
Information Retrieval
Communities, patterns, emotions, interests, intents
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
RQ3
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TeLLNet
Detection Define time intervals based on events of communities
πππ‘πππ£πππ = πππππππ , πππ‘πππ where j is an event
Modularity-based community detection Newman and Girvan, 2004
Propinquity algorithm Zhang et al. 2009
Evolution Mapping of communities using modified Jaccard index
πππ πΆππ , πΆππ = maxπΆππβπΆππ
πΆππ,πΆππβπΆππ
πΆππβ₯ π‘βπππ βπππ
Gliwa et al. 2012
Event extraction Asur et al. 2009
Community events: dissolve, form , merge, split, and continue
Node events: appear, disappear, join and leave
Community Detection & Evolution
RQ3
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
Emotional analysis Pennbaker et al. 2007, Calvo and DβMello 2010
Intent analysis Tatu, 2008, KrΓΆll, 2009, Strohmaier et al., 2012
POS tagging + syntactic language patterns
Verb to verb pattern ππ΅1_π‘π_ππ΅2, e.g., learn to calculate
Wh-adverb to verb pattern ππ π΅_π‘π_ππ΅, e.g., how to estimate
Learning Concepts and Topics Siehndel et al. 2013, d'Aquin and Jay, 2013
Named entities are arguments of information units Grishman and Sundheim, 1996
POS tagging + domain analysis
Linked Open Data Cloud Berners-Lee et al., 2006
Language-based Analysis
Category Examples
posemo awesome, super,
negemo depressβ¦, scary,
anger aggressβ¦, stupidβ¦,
cogmech inferβ¦, problemβ¦,
insight explainβ¦, reasonβ¦,
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
RQ3
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TeLLNet Overview of Case Studies
Modeling Learning Communities in Learning Forums
Competence Management Support for European Teachersβ Communities
Cultural Analysis of Communities in 13 Wikipedia language projects
CommunityMedium (Forum)
usesn 1
Community Media (Project,E-mail, Blog)
uses1 n
TeLLNet
CommunityMedium
(Wiki)usesn 1
originatesfrom
Country 11
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
Modeling Learning Communities in Learning Forums
The language learning forum URCH # posts β 429.000 # users β 21.000 # threads β
68.000, Other datasets with 10β΅ - 4,8x10β΅ edges for testing
User patterns (k-means clustering and SNA) Intent analysis -> learning goals Emotional analysis -> user attitude Named entities of community texts
Modeling
Refinement
Monitoring
Analysis
Petrushyna et al., 2014Petrushyna et al., 2015
A community can be represented by a steeotype model or models from repository
Stakeholders can decide about changes they need to conduct in communities
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
Modeling Learning Communities in Learning Forums
The language learning forum URCH # posts β 429.000 # users β 21.000 # threads β
68.000, Other datasets with 10β΅ - 4,8x10β΅ edges for testing
i* actors: users, threads, forums, user roles, topics of interest Dependencies: user intents, user activities, actor dependencies
User patterns (k-means clustering and SNA) Intent analysis -> learning goals Emotional analysis -> user attitude Named entities of community texts
Simulations using network strategies: reciprocity and preferential attachment
A number of possible community states in future
Modeling
Refinement
Monitoring
Analysis
Petrushyna et al., 2014Petrushyna et al., 2015
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
Architecture for Community Learning Analytics Framework
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
How to Realize Continuous Support of Informal Learning Communities?
01-10.12.2004# posts = 471
# users = 22
# adjacent nodes = 43
# high influence users = 13
# low influence users = 2
need to learnwant to write
take to solve
started to take practice
prepared to take beast
trying to learn stuff
# posts = 226
# users = 20
# adjacent nodes = 15
# high influence users = 4
# low influence users = 4
how to answer
instructed to take writing
supposed to answerplan to take GRE
take to solve
Petrushyna et al., 2015
08-17.12.2004
Models of a learning community in URCH forums
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
Strategies:
Reciprocity only
High Reciprocity low PA
50% Reciprocity and 50% PA
Can Model Simulations Predict Community Evolutions?
initial 30 days later
Simulated behaviors of learners differ according to strategies (reciprocity and preferential attachment (PA)) and activity probabilities (maps)
Betweenness Closeness Clustering Degree
Kolmogorov-Smirnov tests of measure distributions show a better correlation (<.5) between real and simulated community learners with >39 users
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
40% follow life cycle of self-regulated learning in cliques (tightly connected groups) while others need a support
Estimation of Self-Regulated Theory Using Community Analysis
Krenge et al., 2011
Nussbaumer et al., 2011
Thread 1Thread 2
Thread 3
A user of a clique
A non-clique user
in a thread
A clique-user
missing in a
thread
Time
Maintain Profile
Select Resource
Learn
Reflect
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
21 i* experts evaluated i* models oflearning communities: social network analysis (71%) and
intent analysis (90%) are helpful forcreating i* models
community stakeholders canunderstand community situationsbetter using i* models (86%)
emphasizing community requirements for developers (86%)
i* models can be abstract and notstraightforward
Training is required before stakeholderscan use models
Evaluation of Community Analytics Techniques
SocialNetwork Analysis
Community DetectionandEvolution
IntentAnalysis
NamedEntitiesRetrieval
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
Competence Management Support for European Teachersβ Communities
Modeling
Refinement
Monitoring
Analysis
Self-monitoring and self-reflection for teachers Kitsantas, 2002
Other stakeholders refine community situations based on monitoring and analysis
β164K teachers, β20K projects, β39K emails, β35K blog posts Data transformation is required, e. g., β 130K with wrong
country value
Competence indicators for teachers, communities and stakeholders Song et al., 2011
Analysis of different media networks Pham et al., 2012
i* actors: project performance, activity, popularity, e-mail communicating skills, etc.
eTwinning let European teachers cooperate with the means of projects, e-mails, blogs, comments, contact lists, walls, etc. Competence is the knowledge, skills, attitudes, β¦ related to tasks McClelland, 1973
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet How to Support Self-Monitoring of Learners?Reports for teachers and other stakeholder using competence indicators :
project performance (PP) e-mail communication (EC) blog writing (BW)
PP EC BW CW A N
Song et al., 2011
π΄ π‘ = πππππ π‘ +1
2Γ [(π ππππππ ππ’π‘ + πΆπππ‘πππππ‘π¦ππ’π‘ππππππ + π πππππππππππ π‘π‘ +
π πππππΆπππ‘ + π ππππ§ππΆπππ‘ + π πππππΆπππ‘ ],
where xxxπΆππ is a comment in a blog or devoted to a prize or a project
Teacher 1 Teacher 2 Teacher 3 Teacher 4 Teacher 5 Teacher 6
comment writing (CW), activity(A) notability (N)
10
8
6
4
2
0
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
Estimation of Quality of Project Participation Using Community Analysis
0 10 20 30 40 50 60 700
0.2
0.4
0.6
0.8
1
Fre
quency
Number of quality labels
(a) Quality labels and number of projects/blogs+blog posts/contacts/wall posts
Blog
Contact
Project
Wall
0 10 20 30 40 50 60 700
0.2
0.4
0.6
0.8
1
Degre
e
Number of quality labels
(b) Quality labels and degree
Blog
Contact
Project
Wall
0 10 20 30 40 50 60 700
0.2
0.4
0.6
0.8
1
Betw
eenness
Number of quality labels
(c) Quality labels and betweenness
Blog
Contact
Project
Wall
0 10 20 30 40 50 60 700
0.2
0.4
0.6
0.8
1
Clu
ste
ring
Number of quality labels
(d) Quality labels and clustering
Blog
Contact
Project
Wall
Quality labels (QL) are prizes according to eTwinningambassadors (active stakeholders)
Number of QL correlates positively with betweennessof teachers in project networks
Pham et al.,2012
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
TechnicalContribution
Test Cases
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TeLLNet
Accelerating Community Detection and Evolution on Single PC using GPU
Dataset URCH STDocNet
Number of snapshots 378 685
Number of edges β300K β480K
GPU running time 30 min 22 min
CPU running time > 4 h > 3h
Dataset URCH STDocNet
Number of snapshots 1 1
Number of edges 9110 1188
Number of nodes 857 263
GPU running time 30 min 1.5s
CPU running time β2 h 4s
GPU implementationis efficient for bignetworks with > 1Kedges
GPU implementationallows detection ofhuge communitiesusing just ONE! PC
Motivation
Backgroundand context
Methodology
Conclusions and Outlook
Technical Contribution
Test Cases
GPU
CPU
10K 25K 50K 100K
2.5K
2K
1.5K
1K
0.5K
Seconds
Edges
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TeLLNet Contributions and Conclusions
Modeling Refinement Monitoring Analysis
The workflow for Community Learning Analytics:
Toolset for modeling, refinement, monitoring and analysis of informal online learning communities
Support of informal online learning community stakeholders by integrating computer science approach with community of practice theory
A metamodel of learning communities and its stereotype models
Motivation
Backgroundand context
Conclusions and Outlook
Technical Contribution
Test Cases
Methodology
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TeLLNet Contributions in Informal Learning Context
The workflow proposes a structure for analytical investigation of informal learning communities
A toolset for validating learning theoriesβ assumptions
Justifying computer science approaches for community of practice analysis
Abstract modeling of informal learning communities emphasizing human and non-human agents
Validating existing theoretical community patterns
Motivation
Backgroundand context
Conclusions and Outlook
Technical Contribution
Test Cases
Methodology
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TeLLNet Limitations and Follow-up Research
Refinement of the toolset to perform near real-time monitoring, analysis and modeling Derntl et al., 2015
Extension of community analysis tools with other techniques, e.g. prediction models of student success
Involvement of new features and strategies for community simulation
The usage of heterogeneous media: SNSs, Twitter
Motivation
Backgroundand context
Conclusions and Outlook
Technical Contribution
Test Cases
Methodology
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TeLLNet Acknowledgements
To my supervisors
To my family
To my colleagues and friends
To my students
TEE
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Andrew Hilts and Eric Yu: Intentional Modeling of Social Media Design Knowledge for Government-Citizen Communication. In Martin Atzmueller, Andreas Hotho, Markus Strohmaier, and Alvin Chin, editors, Analysis of Social Media and Ubiquitous Data, volume 6904 of Lecture Notes in Computer Science, pp. 20β36. Springer Berlin Heidelberg, 2011.
Matthias Jarke, Ralf Klamma, Gerhard Lakemeyer, and Dominik Schmitz: Continuous, Requirements-Driven Support for Organizations, Networks, and Communities. In Proceedings of the 3rd International i* Workshop, Recife, Brazil, February 11-12, 2008, pp 47β50
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