Network Analysis of Massively Collaborative Creation ofMultimedia Contents
Case Study of Hatsune Miku videos on Nico Nico Douga
Masahiro Hamasaki

Hideaki Takeda

Takuichi Nishimura

National Institute of AIST/ JST, CREST
1-1-1 Umezono, Tsukuba
Ibaraki, Japan

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

National Institute of AIST
/ JST, CREST
1-1-1 Umezono, Tsukuba
Ibaraki, Japanhamasaki@ni.aist.go.jp

takeda@nii.ac.jp

taku_ni4@ni.aist.go.jp

ABSTRACT

1. INTRODUCTION

The World Wide Web supports new styles of creative activities. For this study, we investigate massively collaborative creation via the Web, by which numerous people gather
to evolve their works collaboratively. Nico Nico Douga is avideo sharing website, where many videos are created collaboratively. We speciﬁcally examine Hatsune Miku, a versionof singing synthesizer application software that has inspired
not only song creation but also songwriting, illustration, andvideo editing. As described herein, diﬀerent types of creatorsinteract to create new contents though their social network.
Using tags, we classiﬁed videos and creators on Nico Nico
Douga automatically into four basic categories. Thereby, we
produced a social network from relationships among videos
and creators by analyzing videos’ descriptions. The socialnetwork reveals interesting features. Diﬀerent categories ofcreators serve diﬀerent roles in evolving the network, e.g.,songwriters gather more links than other categories, implying that they are triggers to network evolution. We also
extracted communities from the network and observed different community structures. One is a centralized networkin which a single songwriter is central and others are peripheral. The other is a messier network, in which someillustrators are central, but the centrality is weak.

The Web has become a common platform for communication in our society. As a platform for communication,
the Web presents various advantages over older ones. For
example, it erases the sense of distance because people atany place can mutually communicate. It enables massiveinteractive 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 contents suchas videos. YouTube is a typical example.
Such new features of communication have fostered a newstyle of creative activity. Of these, a very interesting feature
is massively collaborative creation of digital content. Now
people create new content by communicating or collaborating with others via the Web. One new feature of collaboration is the style of participation. Because vastly numerous
people are involved, they often do not know each other. Numerous evolved or inspired versions of the video are createdif a video explores a new idea or catches on. Another difference is the digital re-use of content. 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 , where intense collaborative video creation isoccurring. We speciﬁcally address Hatsune Miku 2 , which isa version of singing synthesizer application software that has
inspired many people to produce various music, picture, andvideo compositions. The Hatsune Miku phenomenon is interesting because diﬀerent creators such as songwriters, songcreators, illustrators, and CG creators interact in projects.
We adopted a method of social networking analysis asa basic 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 (see [4]). In our case, we investigate howdiﬀerent types of creators interact to create new content
through their social network.
The sections of this paper are organized as follows. We

Categories and Subject Descriptors
H.4 [Information Systems Applications]: MiscellaneousGeneral Terms
Human Factors

Keywords
Massively collaborative creation, Community analysis, Video
sharing

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12

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

brieﬂy describe related work in Section 2 and the target of
our analysis—Nico Nico Douga and Hatsune Miku—in Section 3. Subsequently, we explain our data extracted fromthe website and the preliminary process for the analysis described in Section 4. We present the results of our analysis
in Section 5 and summarize this report in Section 6.

2.

RELATED WORKThe analysis of citation relations is a traditional approach
to investigate the development of scientiﬁc activities [6].
Much analysis and visualization of citation networks has
been done recently because large volumes of bibliographic
information are now available [1]. However, few reports
among scientiﬁc papers describe creative activities. Chenget al. [2] investigated data of video posts and networks ofusers and videos on YouTube. Nevertheless, their analysis oflarge amounts of data is interesting, but the analysis is superﬁcial: it does not address the semantics of video creation.
Halvey and Keane [3] made a similar analysis of YouTube.

3.

THE TARGET

Figure 1: Screenshot of Nico Nico Douga. This
video is one of the most popular videos related toHatsune Miku.

3.1 Nico Nico Douga
Nico Nico Douga is the most popular video sharing website
in Japan. Started in December 2006, it now has more than
7.9 million users (1 July, 2008) and has published more than
0.8 million videos (estimated by the authors). The basic
service closely resembles that of YouTube, but it has someunique functions and has gathered many users rapidly. The
most interesting and unique function is the direct overlayingof comments on videos. A user can add comments about aspeciﬁc playback time at a speciﬁc position in the video (seeFig. 1), which gives people a sense of sharing the viewing
experience virtually. Furthermore, the creator can instantly
know which speciﬁc moment or speciﬁc scene is appreciatedby a viewer. It inspires the original creator or other creators
to produce new videos to match such eﬀects.
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.

3.2 Hatsune Miku and Its BoomHatsune Miku is a version of singing synthesizer application software. The singing synthesizer developed by YamahaCorp. enables users to synthesize songs that include singing
by inputting lyrics and melody on computers just like computer music. Hatsune Miku is a version of singing synthesizer applications called Vocaloid2; its uniqueness is that itadopted recorded vocals of a famous anime actress. Initiallymost of the created songs were existing songs, but originalsongs soon appeared.
At the same time, a mascot image of Hatsune Miku hascaught on. There is only a single illustration of Hatsune
Miku: it is printed on the software package. People draw
and post diﬀerent illustrations of Hatsune Miku. Then people started to create videos, such as promotion videos formusicians, with such original songs and drawings. Somepeople even create 3D models of Hatsune Miku and create
3D animation videos.
The mixture of diﬀerent creation types is very interesting.
Hatsune Miku gathered diﬀerent types of creators: songwriters from the computer music ﬁeld, illustrators who comefrom ’doujinshi’ - self-published manga culture - and even
CG creators. Most are amateurs, but some are professionals.
They are stimulated by the work of others.
We categorize creation related to Hatsune Miku before theanalysis.
• (a) Songwriting: Amateur songwriters are eager topromote their songs, but it takes time and money toproduce promotion tapes with professional singers. Vocaloidsolves this problem. Now they can produce sound withvocalizations as computer music. It inspires amateur
songwriters to publish their original songs with Hatsune Miku.
• (b) Song creation: It is not easy to make Hatsune Miku
sing songs naturally. Certain techniques are necessary
to tune Hatsune Miku, but it is fun to tune the software
to create nice singing songs. They vie with each otherto create them.
• (c) Illustration: The image of Hatsune Miku is a typical anime character and attracts anime fans. They
are used to drawing their favorite characters by themselves. They produce many diﬀerent scenes and facial
expressions. Those with more expertise produce animation. They often use illustrations drawn by others
as material for producing their anime. Some produce
2D animation and others 3D animation with 3D CGtools.
• (d) Editing: There are so many Hatsune Miku videos
that some people collect them and produce summary
videos, with ranking programs of Hatsune Miku videos.

4. DATA COLLECTIONIn 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. The metadata include view times, uploaded date,uploader name, tags, and a description. The most popularTable 2: Categories of creation for creators: W signifies Songwriting, C means Songcreation, and I represents Illustration.
Category Creators Sum of #in-links
W284590
529
487
IC642
267
W&C
75351
24
21W&I
44
176
C&I
C&W&I
1736
1,296
349
unknown
total
2,911
2,277

Figure 2: Reference Network
Table 1: The top five videos which have many links.
Rank# ofUpload dateCategory
in-links
of Creation1237
2007/09/20
Songwriting
124
2007/09/04 Song creation
2& Illustration
93
2008/02/34
Illustration3474
2007/12/07
Songwriting555
2007/09/13
Songwriting
video was viewed 4,425,208 times. The average number ofviews of the collected videos was 24,028. These 7,138 videos
were uploaded by 2,911 unique contributors 3 .

5.

THE RESULT OF THE ANALYSIS

5.1 Reference Network
Each video has a title and a description written by thecreator. The description often includes hyperlinks to othervideos that can reﬂect details of the video’s creation. It is
customary on Nico Nico Douga that a creator cites othervideos if a sound, image, or any part of another video isused. 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. Figure 2 shows a part of that network.
Two videos gathered many links around the center of thenetwork. The left video published the most popular original
song for Hatsune Miku. The right one introduced a character called “Hachune Miku” (an infantilized version of theHatsune Miku mascot) with a leek. This video contributed
to making Hatsune Miku popular.
We infer a relation between the creators of videos A and B
when video A has links to video B. In this way, we generated
a network among creators. This network has 2,164 nodes(creators) and 4,368 links (relationships among creators). As
described in this paper, we regard this network as a social
network of creators.

5.2 Category of Creation Activity
In section 3.2, we classiﬁed creative activities related to3

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.

Hatsune Miku into four categories: Songwriting, Song creation, Illustration, and Editing. In this section, we present
the analysis of data based on these categories. Editing ismore of a meta-creation activity than the others are. For
that reason, we used the former three categories.
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 like’cool’, ’great’, and ’music’, but also kinds of creation: ’Original song’, ’Make It Sing’, and ’Make It Dance’. We provide
a set of tags that are important for speciﬁc creation categories. For example, if a video has the tag “Miku OriginalSong”, then we classify it as “Songwriting”. If a video has
the tag “3D Hatsune Miku Project”, then we classify it into
“Illustration”. A video is classiﬁable into two or three categories if it has tags for these categories. We also determine
the creator’s category by aggregating that person’s works.
A single creator is classiﬁable into plural categories if thevideos are classiﬁed into more than a single category 4 .
Among 2,911 creators, we obtained eight categories: threebasic types, four types by combining basic categories, andan unknown category. Furthermore, we manually excluded
179 Editing videos.
Table 3 presents the number of creators and the numberof in-links per creator by these categories. It might be readily apparent that the Songwriting and Song Creation category attracts many links, whereas Illustration does not doso. That fact suggests that Songwriting and Song Creation
triggers creative activities. On the other hand, Illustrationacquires many creators. Illustration activity is good at attracting collaborators.
Figure 3 portrays the relationship among creation categories. In this network, the node size represents the number of creators of each category; the arc width shows thenumber of relationships among creators. We draw an arc
among categories when the relationships number more than30. By following the links inversely, we can observe how
the creative activity is diﬀused among diﬀerent types of creators. Songwriters and Song Creators are the major sourceof the diﬀusion. Then Song Creators (only) and Illustrators
followed the diﬀusion. Illustrators were followed by themselves, which indicates that they diﬀuse Hatsune Miku intheir community.
4Some videos cannot be categorized correctly using thismethod. We checked some top-ranking videos and creatorsand corrected them manually.

Table 3: Structure of the biggest clusters. Thecolumns of Key person and Majority respectively
show categories of key persons and the majority.
# Size
TypeKey person Majority
1161 centered
WI
144
messy
I
2
3118 centered
I&C
I, C
95
messy
I
4
591
centeredII
90centeredW&C
C6779
centered
W
C
856
messy
C,C&I
55
centered
W&C
C
951
messy
I
10

Figure 3: Relationship among categories of creation.

Figure 4: Frequency distribution of in-links per creator on their reference network.

6. CONCLUSIONAs described in this paper, we have investigated how different types of creators interacted in massively collaborative
creation. We extracted and analyzed the social networks ofcreators, revealing some interesting facts, e.g., diﬀerent categories of creators have diﬀerent roles in evolving the network.
Future TV is expected to be interactive, but interactivity
means not merely that the audience becomes active in responding to TV programs even if they join to create content
together. For that reason, what happens in video sharing
sites is what can be expected to happen with future TV.
The results presented in this paper are informative for future TV in this sense.

7. ACKNOWLEDGMENTS
This research was partially supported by KAKENHI.
Figure 4 depicts a frequency distribution of in-links per
creator on each creation category. The distributions aremutually similar, but the highest number of in-link for SongCreation is lower and the curve is shallower than the others
are, which indicates that the Song Creation Community isless centralized than others are.

5.3 Community on Creators Network
In this section, we analyze the creators’ community. The
term ”creators’ community” means a tight group of nodes
within social network of creators. We adopt Newman clustering [5] to detect such communities from the social networkof creators.
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.
We categorized these clusters into two types. Clusters 1,3, 5, and 6 have only one key person who has an extremely
high number of in-links. On the other hand, clusters 2 and 4
do not clearly have a key person. Some creators have many
more in-links than others in their community, but they are
not dominant.
In each case, Songwriting is often a key person, meaning that Songwriting triggers creative activity. On the otherhand, some communities have a key person from the Illustration category. These communities’ development characterizes the Hatsune Miku and 3D modeling tool environment.
Such communities have an important role in fostering creative activity.

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