Network Analysis of an Emergent Massively Collaborative Creation Community-How can people create videos collaboratively without collaboration? Masahiro Hamasaki

Hideaki Takeda

Tom Hope

Takuichi Nishimura

National Institute of
AIST, Japan/ JST CREST
1-18-13 Sotokanda,
Chiyoda-ku, Tokyo, Japan

National Institute ofInformatics (NII)
2-1-2 Hitotsubashi,
Chiyoda-ku, Tokyo, Japan

National Institute of
AIST, Japan/ JST CREST
1-18-13 Sotokanda,
Chiyoda-ku, Tokyo, Japan

National Institute of
AIST, Japan/ JST CREST
1-18-13 Sotokanda,
Chiyoda-ku, Tokyo, Japan

Abstract
The Web technology enables numerous people to collaborate
in creation. We designate it as massively collaborative creation via the Web. It is becoming an important activity suchas Wikipedia and Yahoo! QA. As an example of massively
collaborative creation, we particularly examine video development on Nico Nico Douga, which is a video sharing website that is popular in Japan. We specifically examine videos
on Hatsune Miku, a version of a singing synthesizer application software that has inspired not only song creation but
also songwriting, illustration, and video editing. As describedherein, creators of interact to create new contents though theirsocial network. We analyzed the process of developing thousands of videos based on creators’ social networks. The social network reveals interesting features. Different categories
of creators serve different roles in evolving the network. We
also extracted communities from the network and observed
different community structures and investigated the evolving
nature of the network using motif analysis.

Introduction
The Web was created as a publishing platform, but now it
is becoming a two-way communication platform for people. Thanks to the flexibility of the Web, communications ofvarious types have emerged. As a platform for communications, the Web presents various advantages over older systems. For example, it erases the sense of distance because
people at any place can mutually communicate. It supports
massive interactive collaboration through large-scale BBS
and social tagging, which is impossible in real-world communication channels. It also enables large-scale information
sharing not only of texts but also of various multimedia content such as videos. This activity already produces valuable
digital contents such as Wikipedia and Yahoo! QA which
would never have been born without the Web. We designate this type of activity as massively collaborative creation
where a huge number of people mutually collaborate to create contents on the Web.
Massively collaborative creation on the Web has featuresthat augment traditional collaborative creation; one is thestyle of participation. Vastly numerous people are involved.
c 2009, Association for the Advancement of Artificial
Copyright °Intelligence (www.aaai.org). All rights reserved.

For that reason, they often do not know each other. Numerous evolved or inspired versions of a video can be created if
a video explores a new idea or catches on. Another difference is the digital re-use of contents. Some components ofa work are re-used in evolved or inspired versions, e.g., animage or sound in a video can be re-used in a new versionof the video. Consequently, digital work can be developed
among people via the Web.
We are interested in the development of such digital
works, particularly in the process as it unfolds in a video
sharing website. We selected a video sharing website called
Nico Nico Douga 1 , in which intense collaborative video creation is occurring. We specifically address Hatsune Miku 2 ,which is a version of singing synthesizer application software that has inspired many people to produce various music, picture, and video compositions. The Hatsune Miku
phenomenon is interesting because different creators interact in projects: song creators, illustrators, and CG creators.
We adopted a method of social networking analysis as abasic method to investigate the phenomenon. Applying social network analysis method to web contents has yielded
important results for explicating the structure of interaction
among people. In our case, we investigate how creators ofdifferent types interact to create new contents through theirsocial network.
This paper is an extended version of a preliminary report(Hamasaki, Takeda, and Nishimura 2008). We herein describe results of the analysis in greater detail, in addition to
new results obtained using different analyses.

Targets
Nico Nico Douga
Nico Nico Douga is the most popular video sharing website
in Japan. Started in December 2006, it now has more than
10 million users (Dec, 2008); it has published more than 2.2
million videos. The basic service closely resembles that ofYouTube, but it has some unique functions and has gathered many users rapidly. The most interesting and uniquefunction is the direct overlaying of comments on videos. A
user can add comments about a specific playback time at a
specific position in the video, which gives people a sense of12

http://www.nicovideo.jp/
http://www.crypton.co.jp/mp/pages/prod/vocaloid/cv01.jsp

sharing the viewing experience virtually. Furthermore, thecreator can instantly know which specific moment or specific scene is appreciated by a viewer. That feedback inspires
the original creator or other creators to produce new videos
to match such effects.
In Nico Nico Douga, a popular category of video is a socalled MAD movie. The key feature of a MAD movie isthat the used extracts are often taken from commercial anime
programs. That feature has merit for people because it is fun
to view different versions of popular programs but it is problematic because it often engeders copyright violations. The
birth and development of Hatsune Miku suggests a new direction to MAD movies. In Hatsune Miku videos, extracts
are not taken from commercial programs; instead, the community creates videos.

Hatsune Miku and Its Boom
Hatsune Miku is a version of singing synthesizer application
software. The singing synthesizer program developed by
Yamaha Corp. enables users to synthesize songs that include
singing by inputting lyrics and melody on computers justlike computer music. Hatsune Miku is a version of singing
synthesizer applications called Vocaloid2; its uniqueness isthat it adopted recorded vocals of a famous anime actress.
Amateur songwriters can publish their songs with Hatsune
Miku. However Hatsune Miku affected them more. They
regard Hatsune Miku as a pop singer so that they write andpublish songs that are suitable for her.
Moreover, a mascot image of Hatsune Miku has caught
on. There is only a single illustration of Hatsune Miku: itis printed on the software package. People draw and post
different illustrations of Hatsune Miku. Then people started
to create videos, such as promotion videos for musicians,
with such original songs and drawings.
Then videos are created in the manner of MAD movies.
Already published videos are used as materials to create
new videos. Fundamentally, creators welcome it because
it indicates that their works are valuable to others. Consequently, many videos are created and published as collaborative works.
The mixture of different creation types is very interesting.
Hatsune Miku gathered creators of different types: songwriters from the computer music field, illustrators who comefrom self-published manga culture (’doujinshi’) and even
CG creators. Most are amateurs, but some are professionals. They are stimulated by the work of others.
We found the following types of creative activity related
to Hatsune Miku.
• (a) Songwriting: Amateur songwriters are eager to promote
their songs, but it takes time and money to produce promotional
tapes with professional singers. Using Vocaloid, they can produce sound with vocalizations as computer music.
• (b) Song creation: It is not easy to make Hatsune Miku singsongs naturally. Certain techniques are necessary to tune Hatsune Miku, but it is fun to tune the software to create nice songs
for singing. Contributors mutually compete to create them.
• (c) Illustration: The Hatsune Miku image is that of a typicalanime character which attracts anime fans. They are used to
drawing their favorite characters by themselves. They produce

many different scenes and facial expressions. They even produce different versions of the character such as Hachune Miku
(an infantile version of Hatsune Miku) and Yowane Haku (a pun
or a fainthearted version).
• (d) Editing: There are so many Hatsune Miku videos that somepeople collect them and produce summary videos, with ranking
programs of Hatsune Miku videos.

We will analyze the creative activity on Hatsune Miku using the creation categories described above.

Results of Analyses
Data Collection
In all, 36,709 videos had the tag ’Hatsune Miku’ on Nico
Nico Douga (31 May 2008). From among them, we selected
7,138 videos that had been viewed more than 3,000 times asdata for this study. We crawled their metadata during 1–
5 June 2008. These 7,138 videos were uploaded by 2,911
unique contributors 3 .

Reference Network
Each video has a title and a description written by the creator. The description often includes hyperlinks to othervideos that can reflect details of the video’s creation. A customary practice on Nico Nico Douga is that a creator cites
other videos if a sound, image, or any part of another video
is used. By tracing these hyperlinks, we generated a reference network of videos. Among the collected videos, 4,585
videos include hyperlinks in the description; we obtained
12,507 links among videos.
We infer a relation between the creators of videos A and
B when video A has links to video B. Consequently, we
generated a network among the creators. This network has
2,920 nodes (creators) and 2,757 links (relations among thecreators). As described in this paper, we regard this network
as a social network of creators.

Category of Creation Activity
We presented four categories for activity related to Hatsune
Miku movie creation: Songwriting, Song creation, Illustration, and Editing. In this section, we present analyses ofdata based on these categories. Because the editing activity
here is excerpting and combining multiple videos, it does notcommit the contents of videos. For that reason, we used theformer three categories. Therefore, we manually excluded
179 Editing videos of 7,281 videos.
We classified videos and creators into creation categories
automatically using tags for videos. Tags on Nico Nico
Douga include not only review or content categories, butalso those indicating the kind of creation. We provide a setof tags that are important for specific creation categories.
The video is classified into these categories simultaneously
if a video has tags of different categories. We also determine
the creator’s category by aggregating that person’s works.
3

On Nico Nico Douga, only the uploader is named. That person
is not necessarily the creator of the video. However, for this study,
we regard an uploader as the video creator.

Table 1: Categories of creation for creators: W signifies
Songwriting, C means Songcreation, and I represents
Illustration. N and Ein respectively denote the number ofcreators and in-links.
Category
W
I
C
W&C

N
284
529
642
75

Ein
590
487
267
351

Category
W&I
C&I
C&W&I
unknown

N
24
44
17
1,296

Ein
21
176
36
349

A single creator is classified into multiple categories if thevideos are classified into more than a single category 4 .
Among 2,911 creators, we obtained eight categories:
three basic four by variously combining the basic categories,
and an unknown category. Table 1 presents the number ofcreators and the number of in-links per creator by these categories. It might be readily apparent that the Songwriting
and Song Creation category attract many links, although Illustration does not. That fact indicates that Songwriting andSong Creation triggers creative activities. On the other hand,
Illustration acquires many creators. Illustration activity isgood at attracting collaborators.

Community on Creators Network
In this section, we analyze the creators’ community. The
term ”creators’ community” means a tight group of nodes
within a social network of creators. We adopt Newman clustering to detect such communities from the social network ofcreators.
Newman clustering generated 83 clusters (communities)
from the social network of creators. We especially investigated 10 clusters of which the size is greater than 50. Table
2 presents the parameters of these 10 clusters.
In Table 2, Size means the number of creators in the cluster. Centralization shows an index of the centrality of a
network in terms of degree (Freeman 1978). χ2 shows a degree of bias of tags in the clusters. When creator A has avideo tagged B, we regard that creator A is tagged B. Then
we can find the distribution of creator’s tags in the cluster.
A category of the key person (K-Cat) means the creative
category of the key person. We set a node that has the mostlinks as a key person of a cluster. However, we do not set
a node as a key person if the number of links of the node isless than 10 percent of the whole links of the cluster. In thiscase, there is no key person exists in the cluster. A categoryof majority (M-Cat) is the most popular creative category ofcreators in the cluster.
We observed that there are two types of community fromthe viewpoint of network structure: a centralized communityand non-centralize community. The former includes clusters
1, 3, 5, and 6 in which there is only a single key person withan extremely high number of in-links. The latter includes
clusters 2 and 4 which do not clearly have key persons.
The key person in a community is often of the Songwriting category, which indicates that Songwriting triggers cre4

Some videos cannot be categorized correctly using thismethod. We checked some top-ranking videos and creators andcorrected them manually.

Table 2: Parameters of the biggest clusters. Columns of KCat and M-Cat respectively show categories of key persons
and the majority.
#

Size

1
2
3
4
5
6
7
8
9
10

161
144
118
95
91
90
79
56
55
51

Centralization(×10−3 )
4.293
0.080
5.257
1.868
5.897
7.055
5.164
3.012
6.923
4.000

χ2

K-Cat

M-Cat

2130.51747.3
1921.01857.72799.92333.7
1942.81797.12079.61761.1

W
I&C
I
W&C
W
W&C
-

I
I
I, C
I
I
C
C
C,C&I
C
I

ative activity. We also find that some communities have key
persons with the Illustration category. In such communities,
new ideas are developed intensively such as a new characterizations of Hatsune Miku and a 3D modeling tool. Suchcommunities contribute to widening the world of Hatsune
Miku and stimulate additional creative activities.
The results of the community-based analyses strengthen
and refine the conclusion from the category-based analysis
described in the previous subsection. Two types of collaboration are identifiable. One type originates with Songwriting creators which involves Song Creation and Illustration
creators. It is the birth of a creative idea and diffusion of a
new topic. The other is collaboration among Illustration creators, where creators are mutually dependent. It is the next
phase in which the diffusion takes place among Illustration
creators.
Apart from the diffusion process, we found that centralization of the network and localization of the tag are mutually correlated. The correlation coefficient among centralization and χ2 is 0.63. It represents a strong positive correlation, meaning that a strongly centralized community tends
to have community-specific tags that are used in many creators in the community and vice versa. It indicates that tag
analysis may be used to identify communities. In fact, thatis good news because identifying links has high costs and isoften impossible.

The Analysis Network Motif
Motif analysis is a method to investigate particular social
dynamics using small local patterns of a network (Milo et
al. 2002). In motif analysis, the hypothesis that networks
display certain patterns, termed ”network motifs,” at muchhigher frequency than expected in randomized networks isused. We applied motif analysis to our creators’ social network (Fig. 1). We also present the motifs of the two social networks for comparison. One is the social network ofprison inmates (MacRae 1960) as analyzed by Milo. The
other is the social network of a QA site as analyzed by
Adamic (Adamic et al. 2008).
The normalized z-scores of 13 network motifs are presented in Fig. 1, the motif of which normalized z-score ishigh, meaning that it is a characteristic pattern of the net-

Figure 1: Results of motif analyses with three social networks. The networks are as follows (where N and E respectively denote the number of nodes and links): (i) creators’
citation network on Nico Nico Douga (niconico N = 1454,
E = 2339); (ii) a users’ question and answer relationshipnetwork on the Wrestling community of YahooQA (yahooN = 9, 959, E = 56, 859); (iii) a friendship network ofprison inmates (prison N = 67, E = 182).
work. We used the motif analysis tool FANMOD 5 to carryout our motif analysis. In Fig. 1, our social network shows
no triad 140 and 238. The absence of triad 140 arises fromthe fact that our social network does not include such a local
pattern. On the other hand, the absence of triad 238 arises
from the fact that the random network has no such local pattern because the number of links to the number of nodes is
too small.
Figure 1, shows that three results are fundamentally similar. It is clearly different from other networks such as WWW(Milo et al. 2002). However, each network has a different
highest scored motif from the others. It is triad 36 in the caseof our network, triad 238 in the case of the prison network,and triad 38 in the QA cite network.
The prison network is based on real world communication, althought others are based on online interaction. It
is probably easier than others to generate a complete graph(triad 238) in the network. Adamic interpreted the meaningof triad 38 in the QA site network as follows. In triad 38, one
person was helped by two others, but one helper has helped
the other helper.
A star graph (triad 36) in our network is important in comparison to the other social networks. In this motif, a user iscited by two others; no relation exists anymore, which indicates that a few popular creators attract many non-popular
creators.
Results show that that creators’ social network has characteristics resembling those of other social networks. Furthermore, its remarkable characteristic is the existence of popular nodes (trigger persons). How did such a network evolve?
The next question is how this characteristic is formed. We
investigated the evolutionary nature of the network to answer the question. To investigate this point, we generated
networks in the specific periods and compared them. We
split the whole period into three. We analyzed motifs ofthese networks (Fig. 2).
The characteristic triad (triad 36) is exaggerated as time5

http://theinf1.informatik.uni-jena.de/ wernicke/motifs/

Figure 2: Results of motif analysis with three times of networks. The networks are as follows: (i) Sep. 2007 (N =240, E = 296). (ii) Dec. 2007 (N = 976, E = 1450). (iii)
May 2008 (N = 1, 454, E = 2339).
goes by. The initial network is rather similar to the prison
network. It indicates that the initial collaboration is as close
as that in the real world. The participants tend to cite to eachother’s work. However, the late participants do not. They areless cited and merely cite the previous creators. It is interesting that the style of collaboration is shifted from a close one
to a sparse one even in such a purely online environment. It
might be a hint indicating when and why the network bursts
in online environments.

Conclusion
We have investigated how different types of creators interacted in massively collaborative creation. We extracted andanalyzed the social networks of creators, revealing some interesting facts. Social media are expected to be interactive, but interactivity is not simple. As described herein,
the different types of creators form interactions of different
types. Interaction also differs depending on the evolution.
We should take that point into account when designing newsocial media, particulary those which support creative activity.

Acknowledgments
This research was partially supported by KAKENHI.

References
Adamic, L. A.; Zhang, J.; Bakshy, E.; and Ackerman, M. S.
2008. Knowledge sharing and yahoo answers: Everyone
knows something. In Proceedings of WWW2008.
Freeman, L. C. 1978. Centrality in social networks conceptual clarification. Social Networks 1:215–239.
Hamasaki, M.; Takeda, H.; and Nishimura, T. 2008. Network analysis of massively collaborative creation of multimedia contents: case study of hatsune miku videos on nico
nico douga. In Proceeding of UXTV ’08, 165–168. New
York, NY, USA: ACM.
MacRae, D. 1960. Direct factor analysis of sociometricdata. Sociometry 23:360–371.
Milo, R.; Itzkovitz, S.; Kashtan, N.; Levitt, R.; Shen-Orr,
S.; Ayzenshtat, I.; Sheffer, M.; ; and Alon, U. 2002. Superfamilies of evolved and designed networks. Science(5594):824–827.