Understanding massive artistic cooperation: the case of Nico Nico Douga
Many online social networks have been studied in the last decade, giving us insights into the way people diffuse information, communicate, and organize themselves. In this article, we focus on the emergent organization in massive artistic cooperation. We study the creation process of complex music videos in a platform called Nico Nico Douga. We give insights into three aspects of emergent organization:
The relation between popularity (in terms of view) and influence on the cooperation process.
The specialization of creators.
The organization of the network of citation.
KeywordsSocial network analysis Massive cooperation Artistic cooperation Nico Nico Douga Peer production Online social networks
- Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the fourth ACM international conference on web search and data mining, pp 65–74, ACMGoogle Scholar
- Cazabet R, Takeda H (2014) Understanding mass cooperation through visualization. In: Proceedings of the 25th ACM conference on hypertext and social media, pp 206–211, ACMGoogle Scholar
- Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring user influence in twitter: the million follower fallacy. ICWSM 10:10–17Google Scholar
- Davis JA, Leinhardt S (1967) The structure of positive interpersonal relations in small groups. Darthmouth CollegeGoogle Scholar
- Duguid P (2006) Limits of self-organization: peer production and “laws of quality”. First Monday 11(10) (2006). http://firstmonday.org/ojs/index.php/fm/article/view/1405/1323
- Hamasaki M, Goto M (2013) 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, p 4, ACMGoogle Scholar
- Haythornthwaite C (2009) Crowds and communities: light and heavyweight models of peer production. In: HICSS’09. 42nd Hawaii International Conference on system sciences, 2009, pp 1–10, IEEEGoogle Scholar
- Ley M (2002) The dblp computer science bibliography: evolution, research issues, perspectives. In: Laender AHF, Oliveira AL (eds) String processing and information retrieval. Springer, Berlin, Heidelberg, pp 1–10Google Scholar
- Remy C, Pervin N, Toriumi F, Takeda H (2013) Information diffusion on twitter: everyone has its chance, but all chances are not equal. In: 2013 International Conference on signal-image technology & internet-based systems (SITIS), pp 483–490, IEEEGoogle Scholar
- Tang J, Zhang J, Yao L, Li J, Zhang L, Su Z (2008) Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 990–998, ACMGoogle Scholar
- Toriumi F, Sakaki T, Shinoda K, Kazama K, Kurihara S, Noda I (2013) Information sharing on twitter during the 2011 catastrophic earthquake. In: Proceedings of the 22nd international conference on World Wide Web companion, pp 1025–1028. International World Wide Web Conferences Steering CommitteeGoogle Scholar
- Wilkinson DM (2008) Strong regularities in online peer production. In: Proceedings of the 9th ACM conference on electronic commerce, pp 302–309, ACMGoogle Scholar
- Yang J, Counts S (2010) Predicting the speed, scale, and range of information diffusion in twitter. ICWSM 10:355–358Google Scholar
© Springer-Verlag Wien 2016