RWTH Aachen University researchers developed PALADIN, a Pattern Language for Analyzing Disturbances in digital social Networks. PALADIN uses a graph-based model and pattern language to automatically analyze social networks for recurring disturbance patterns. It represents actors, media, artifacts and dependencies in a social network. PALADIN was tested on 10 disturbance patterns over 119 social network instances with over 17,000 individuals. The results showed PALADIN could detect different disturbance patterns and provide insights to network administrators. Future work will focus on interoperability, visualization of multidimensional disturbances, and integrating social network simulation.

1. 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 Klamma Marc Spaniol Master Thesis Final Presentation

7. State of the Art Digital Social Networks Projects 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 Individuals Friend-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 List COMB [Boudourides et al. 2002] Analysis Approach Actors Media

9. Digital Social Network Model of Digital Social Networks Digital Media I* Dependencies Members Artefacts Member Network

14. Network Coordinator Gatekeeper Hub Member Iterant Broker URL isA isA isA Coordination Artefact Communication Model of Digital Social Networks I* Dependencies Example isA

15. State of the Art Pattern 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 Schema Synopsis, 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-Learning E-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 Structure Domain

18. 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[ thread 1 , message 1 | (thread 1 .author 1 != troll) & (message 1 .author = troll & message 1 .posted = thread 1 ]))])]) 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 Language Sample Pattern

21. Pattern Language Algorithm for Pattern Application 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 Digital Social Network

26. Conclusion 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 Individuals Friend-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 Analysis Developers, Software Components Eclipse IDE, CVS Repository Ariadne [de Souza et al. 2004] Social Network Analysis, Statistics Individuals, Mails, Threads, Genres Mailing List COMB [Boudourides et al. 2002] Analysis Approach Actors Media