Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/2631775.2631818acmconferencesArticle/Chapter ViewAbstractPublication PageshtConference Proceedingsconference-collections
short-paper

Understanding mass cooperation through visualization

Published: 01 September 2014 Publication History

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.

References

[1]
C. Aguiton and D. Cardon. The strength of weak cooperation: an attempt to understand the meaning of web 2.0. Communications & Strategies, (65), 2007.
[2]
D. Auber. Tulip, a huge graph visualization framework. In Graph Drawing Software, pages 105--126. Springer, 2004.
[3]
M. Bastian, S. Heymann, and M. Jacomy. Gephi: an open source software for exploring and manipulating networks. In ICWSM, pages 361--362, 2009.
[4]
S. Bender-deMoll and D. A. McFarland. The art and science of dynamic network visualization. Journal of Social Structure, 7(2):1--38, 2006.
[5]
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.
[6]
B.-J. Breitkreutz, C. Stark, M. Tyers, et al. Osprey: a network visualization system. Genome Biol, 4(3):R22, 2003.
[7]
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.
[8]
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.
[9]
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.
[10]
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.
[11]
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.
[12]
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.
[13]
T. Kamada and S. Kawai. An algorithm for drawing general undirected graphs. Information processing letters, 31(1):7--15, 1989.
[14]
H. Kenmochi. Vocaloid and hatsune miku phenomenon in japan. Proc. of InterSinging 2010, pages 1--4, 2010.
[15]
M. Ley. The dblp computer science bibliography: Evolution, research issues, perspectives. In String Processing and Information Retrieval, pages 1--10. Springer, 2002.
[16]
M. Rosvall and C. T. Bergstrom. Mapping change in large networks. PloS one, 5(1):e8694, 2010.
[17]
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.
[18]
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.
[19]
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.
[20]
J. W. Tukey. Exploratory data analysis. 1977.
[21]
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.
[22]
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.

Cited By

View all
  • (2016)Understanding massive artistic cooperation: the case of Nico Nico DougaSocial Network Analysis and Mining10.1007/s13278-016-0323-36:1Online publication date: 23-Mar-2016
  • (2015)Characterizing the nature of interactions for cooperative creation in online social networksSocial Network Analysis and Mining10.1007/s13278-015-0284-y5:1Online publication date: 21-Jul-2015

Index Terms

  1. Understanding mass cooperation through visualization

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    HT '14: Proceedings of the 25th ACM conference on Hypertext and social media
    September 2014
    346 pages
    ISBN:9781450329545
    DOI:10.1145/2631775
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    In-Cooperation

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 01 September 2014

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. complex networks
    2. dy- namic analysis
    3. mass cooperation
    4. visualization

    Qualifiers

    • Short-paper

    Conference

    HT '14
    Sponsor:

    Acceptance Rates

    HT '14 Paper Acceptance Rate 49 of 86 submissions, 57%;
    Overall Acceptance Rate 378 of 1,158 submissions, 33%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 18 Nov 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2016)Understanding massive artistic cooperation: the case of Nico Nico DougaSocial Network Analysis and Mining10.1007/s13278-016-0323-36:1Online publication date: 23-Mar-2016
    • (2015)Characterizing the nature of interactions for cooperative creation in online social networksSocial Network Analysis and Mining10.1007/s13278-015-0284-y5:1Online publication date: 21-Jul-2015

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media