Remy Cazabet
Hideaki Takeda
ABSTRACT
We present a new type of visualization designed to help the understanding of inner mechanisms of mass cooperation. This type of cooperation is ubiquitous nowadays, not only in Online Social Networks, but also in many other situations, such as scientific research on a worldwide scale. Mass cooperation is also at the source of most complex systems. One problem to which researchers are confronted to when they study such cooperation is to build an intuitive representation of what is happening. Many tools and metrics exist to study the results of the cooperation, but sometimes, these metrics can be misleading if one doesn't really observe what the cooperation process really looks like. The main proposition of this paper is a visualization of the cooperation flow. The novelty of our approach is to represent the internal structure of the cooperation in a longitudinal perspective. Through examples, we present how one can form a rich understanding of what form the cooperation takes in a given context, and how this understanding can help to formulate hypothesis which can consequently be studied with appropriate tools such as statistical analysis.
- C. Aguiton and D. Cardon. The strength of weak cooperation: an attempt to understand the meaning of web 2.0. Communications & Strategies, (65), 2007.Google Scholar
- D. Auber. Tulip, a huge graph visualization framework. In Graph Drawing Software, pages 105--126. Springer, 2004.Google ScholarCross Ref
- M. Bastian, S. Heymann, and M. Jacomy. Gephi: an open source software for exploring and manipulating networks. In ICWSM, pages 361--362, 2009.Google Scholar
- S. Bender-deMoll and D. A. McFarland. The art and science of dynamic network visualization. Journal of Social Structure, 7(2):1--38, 2006.Google Scholar
- D. Boyd, S. Golder, and G. Lotan. Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In System Sciences (HICSS), 2010 43rd Hawaii International Conference on, pages 1--10. IEEE, 2010. Google ScholarDigital Library
- B.-J. Breitkreutz, C. Stark, M. Tyers, et al. Osprey: a network visualization system. Genome Biol, 4(3):R22, 2003.Google ScholarCross Ref
- R. Cazabet, N. Pervin, F. Toriumi, and H. Takeda. Information diffusion on twitter: everyone has its chance, but all chances are not equal. In Signal-Image Technology & Internet-Based Systems (SITIS), 2013 International Conference on, pages 483--490. IEEE, 2013. Google ScholarDigital Library
- R. Cazabet, H. Takeda, M. Hamasaki, and F. Amblard. Using dynamic community detection to identify trends in user-generated content. Social Network Analysis and Mining, 2(4):361--371, 2012.Google ScholarCross Ref
- A. Frick, A. Ludwig, and H. Mehldau. A fast adaptive layout algorithm for undirected graphs (extended abstract and system demonstration). In Graph Drawing, pages 388--403. Springer, 1995. Google ScholarDigital Library
- M. Hamasaki and M. Goto. Songrium: a music browsing assistance service based on visualization of massive open collaboration within music content creation community. In Proceedings of the 9th International Symposium on Open Collaboration, page 4. ACM, 2013. Google ScholarDigital Library
- M. Hamasaki, H. Takeda, and T. Nishimura. Network analysis of massively collaborative creation of multimedia contents: case study of hatsune miku videos on nico nico douga. In Proceedings of the 1st international conference on Designing interactive user experiences for TV and video, pages 165--168. ACM, 2008. Google ScholarDigital Library
- S. Havre, E. Hetzler, P. Whitney, and L. Nowell. Themeriver: Visualizing thematic changes in large document collections. Visualization and Computer Graphics, IEEE Transactions on, 8(1):9--20, 2002. Google ScholarDigital Library
- T. Kamada and S. Kawai. An algorithm for drawing general undirected graphs. Information processing letters, 31(1):7--15, 1989. Google ScholarDigital Library
- H. Kenmochi. Vocaloid and hatsune miku phenomenon in japan. Proc. of InterSinging 2010, pages 1--4, 2010.Google Scholar
- M. Ley. The dblp computer science bibliography: Evolution, research issues, perspectives. In String Processing and Information Retrieval, pages 1--10. Springer, 2002. Google ScholarDigital Library
- M. Rosvall and C. T. Bergstrom. Mapping change in large networks. PloS one, 5(1):e8694, 2010.Google ScholarCross Ref
- P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research, 13(11):2498--2504, 2003.Google ScholarCross Ref
- J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su. Arnetminer: extraction and mining of academic social networks. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 990--998. ACM, 2008. Google ScholarDigital Library
- F. Toriumi, T. Sakaki, K. Shinoda, K. Kazama, S. Kurihara, and I. Noda. Information sharing on twitter during the 2011 catastrophic earthquake. In Proceedings of the 22nd international conference on World Wide Web companion, pages 1025--1028. International World Wide Web Conferences Steering Committee, 2013. Google ScholarDigital Library
- J. W. Tukey. Exploratory data analysis. 1977.Google Scholar
- F. B. Viégas, M. Wattenberg, and K. Dave. Studying cooperation and conflict between authors with history flow visualizations. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 575--582. ACM, 2004. Google ScholarDigital Library
- Y. Zhu and D. Shasha. Efficient elastic burst detection in data streams. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 336--345. ACM, 2003. Google ScholarDigital Library
Index Terms
- Understanding mass cooperation through visualization
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