1. The document discusses how communities shape knowledge structures through media and discusses case studies of disturbances in digital social networks. 2. It presents models for analyzing the evolution of scientific communities and factors that contribute to community success. 3. Methods are proposed for expert finding in communities by analyzing storytelling and recommending experts based on their values and knowledge in the community.

1. Ralf Klamma RWTH Aachen University KASW Workshop, I-Media, September 3, 2008 Community-Oriented Knowledge Acquisition and Analysis

3. Learning & Knowledge Management Individual / Community Perspective [Nonaka & Takeuchi, 1995] [Ullman, 2004] Semantic Knowledge semiotic concepts documentation Verbal words linguistic data Non-verbal image, icon, index video blogs, diagrams, images, photographies Episodic Knowledge memory of experiencing past episodes web blogs, narratives Declarative Knowledge Procedural Knowledge sensomotoric skills, procedural scripts non-documented routines and operations

4. Semiotics in the Tradition of Ferdinand de Saussure (1957 - 1913) comprehension / articulation activation of community information system human neural network Artifacts of community information system in presentia in absentia performance Parole competence Langue

5. Hypotheses 1. A semiotic knowledge system is dynamic and it changes every time it is activated. 2. The meaning of a concept is determined by how it interacts with other concepts and by how it can be distinguished from other concepts in the knowledge system. (Positive and Negative Knowledge) 3. The knowledge system is carried by a material medium. The modality of the medium influences knowledge structures.

8. CESE: Multi-lingual Cross-Media System Published in: DS-NELL 2000, ICALT 2002, ICWL 2002, WWW 2003

9. Research Approach: Reflective Learning Network Collaborative adaptive learning network Mining tools for Communities Measure, Analyse, Simulate Social Software Development Assessment requirements for Communities Support evolving learning communities (repeated assessment of community requirements) Based on Preece 2001, cf. I-KNOW 2006 for details

11. Simplified Meta Model for ANT using Latour Actor Attribute has isA isA Latour: On Recalling ANT , 1999 Klamma, Spaniol, Cao: A model for social software, IJKL 2007 Member Network Learning Service Medium Artifact stores creates is affected by belongs go represents consumes performs ranks … Match Retrieval Browse Search

15. Pattern Discovery Process Digital Social Network 1. Set pattern parameters 2. Instantiate disturbances 3. Evaluate disturbances 4a. Change Pattern Parameters 4b. Apply Pattern Solution Pattern Disturbance Variables Pattern Template Disturbance Variables Pattern Parameters Pattern Template Instance Pattern Instance Disturbance Variables Pattern Parameters Forces Force Relations Rationale Dependencies Description Solution Pattern Relations Disturbance Instances Variables Pattern Parameters

16. PALADIN Case Study 10 patterns of disturbance over 119 social network instances, 17359 individuals, 215 345 mails Occurs in big networks where the members are distributed in different clusters. 40 No Leader Occurs for members having neighbors with only one contact. 67 Structural Hole Occurs in large networks where disconnected subnetworks exist. Scalability is necessary. 13 Independent Discussions The pattern occurs in the network centered around a member. 37 Leader Spammers can be found often in discussion groups. False positives exist. 86 Spammer Troll occurs very rarely in cultural communities. True negatives exist. 2 Troll Occurs in small networks. The effects of the lack of an answering person must be further checked with content analysis. 61 No Answering Person The existence implies that the network is not popular. 67 No Questioner The existence implies little communication in the network. 76 No Conversationalist The pattern finds out topics which were very important for certain period of time. Scalability is necessary. 22 Burst Remarks Occurrences Pattern

18. AERCS: Evolution of Scientific Communities

19. Evolution of community VLDB 1990 VLDB 1995 VLDB 2000 VLDB 2006

20. Community visualization – ACM SIGMOD example ACM SIGMOD