http://www.uni-koblenz-landau.de/koblenz/fb4/AGStaab/Persons/Gottron and follow him on http://twitter.com/tgottron.
Selected topic: Of particular interest in the context of the ROBUST project are social networks and online communities in a business setting. Business communities represent a valuable asset for multiple reasons: they form complex and rich information ecosystems, they provide an active communication channel with business partners, employees or the general public and finally they quite often reflect a substantial monetary investment. Among many other topics, the institute WeST has investigated several interesting aspects of information propagation and flow in online communities [1,2,3], how content and sentiment diversification can be used to get a broader overview of the topics discussed by the users  and how to manage community news streams efficiently for millions of users. Current and ongoing works address the potential of using simulation based approaches to understand and predict user interaction with content items and how changes of the policies implemented in a social network platform can affect these interactions.
 N. Naveed, T. Gottron, J. Kunegis, and A. Che Alhadi, “Bad news travel fast: A content-based analysis of interestingness on twitter,” in WebSci ’11: Proceedings of the 3rd International Conference on Web Science, 2011. in preparation.
 N. Naveed, T. Gottron, J. Kunegis, and A. Che Alhadi, “Searching microblogs: Coping with sparsity and document quality,” in CIKM’11: Proceedings of 20th ACM Conference on Information and Knowledge Management, pp. 183–188, 2011.
 T. Gottron, O. Radcke, and R. Pickhardt, “On the temporal dynamics of influence on the social semantic web,” in CSWS’12: Proceedings of the Chinese Semantic Web Symposium, 2012.
 N. Naveed, T. Gottron, S. Sizov, and S. Staab, “Freud: Feature-centric sentiment diversification of online discussions,” in WebSci ’12: Proceedings of the 4th International Conference on Web Science, 2012.
 R. Pickhardt, T. Gottron, A. Scherp, S. Staab, and J. Kunze, “Efficient graph models for retrieving top-k news feeds from ego networks,” in SocialCom’12: Proceedings of ASE/IEEE International Conference on Social Computing, pp. 123–133, 2012.