Collaboration in N-th Order Derivative Creation
Shiori Hironaka1 , Kosetsu Tsukuda2 , Masahiro Hamasaki2 , and Masataka Goto2
12Toyohashi University of Technology, Japan
National Institute of Advanced Industrial Science and Technology (AIST), Japans143369@edu.tut.ac.jp {k.tsukuda, masahiro.hamasaki, m.goto}@aist.go.jp

Abstract
In derivative creation activity, where new content is createdbased on existing content, it has become popular for multiple creators to collaborate to create new derivative content.
In this paper, we analyze the collaboration of music-related
derivative videos on a video sharing service. Specifically, byusing 83,496 collaborative videos created by 22,841 creators,
we analyze the collaboration from the following two viewpoints: video popularity and creator activity. Our analysis results showed that collaborative videos tend to become more
popular than non-collaborative ones, the collaboration is nota one-off activity but a continuous one, and creators who havecollaboration experience are active for a longer time than inexperienced creators, etc.

1

Introduction

On video sharing services such as YouTube1 , not only professional creators but also amateur creators create and post
various kinds of videos. For amateur creators in particular, since it is not always easy to create new content fromscratch, it is popular to base new derivative content on existing content (Hamasaki, Takeda, and Nishimura 2008). For
example, on YouTube, we can see a lot of derivative videos
in which creators dance to an existing song or perform acover of it (Liikkanen and Salovaara 2015). Such creation
activity where new derivative works are created from an existing work one after another is called N-th order derivativecreation (Goto 2012). As we will show in Section 3.2, collaborations between creators to create content in N-th orderderivative creation are also common. In such content, for example, multiple creators sing a song together or one creatorplays the piano and the other one dances to the piano.
The collaboration activity is not limited to video creation;
it is also common in various situations in human society(e.g., a development project in a company, co-authorship of a
research paper, and music activity of a band). Through suchcollaborations, people have created content such as products, articles, and songs. Since understanding the collaboration is important from the social scientific viewpoint, several studies have analyzed the collaboration activities to create content (Hu, Chen, and Luan 2014; Luther et al. 2010).
Copyright c 2018, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
1
https://www.youtube.com/

There are some studies about N-th order derivative creationsuch as analyzing the citation relationships between content (Hamasaki, Takeda, and Nishimura 2008) and detecting characteristics of content that is more likely to be usedas source content to create new content (Hill and MonroyHernández 2012; Calefato, Iaffaldano, and Lanubile 2018).
However, to the best of our knowledge, no study has analyzed the collaboration in N-th order derivative creation interms of content popularity and creator activity.
In light of the above, in this paper, we analyze the collaborations between creators by using derivative creation data ofmusic content posted to Niconico2 , which is one of the mostpopular video sharing services in Japan. Our dataset consistsof 83,496 collaborative videos created by 22,841 creators.
We analyze the data based on the following two viewpoints.
• Video popularity: for a creator, the popularity of his/hervideos is important because he/she will be motivated tocreate new videos if his/her videos have become popular
on a video sharing service. Hence, we analyze the collaboration in terms of video popularity. (Section 4)• Creator activity: for video sharing services, it is importantthat creators act (i.e., create content) for a long time because this leads to the continuous growth of the service.
Therefore, we analyze the collaboration in terms of creators’ activity. (Section 5)
We believe our work provides valuable insights for bothcreators and video sharing services. For creators, our analysis results show that collaborative videos tend to become
more popular than non-collaborative ones; while for video
sharing services, we reveal that creators who have collaboration experience act for a longer time than inexperiencedcreators.

2Related Work

Studies dealing with collaboration in human society
have been conducted in various fields such as company
projects (Inoue and Liu 2015; Muller et al. 2013) and research paper writing (Hu, Chen, and Luan 2014). For example, Hu, Chen, and Luan (Hu, Chen, and Luan 2014)
reported a positive correlation between the number of authors who have collaborated with another author and the2

http://www.nicovideo.jp

3
3.1

DatasetDevelopment

To analyze the collaboration in N-th order derivative creation, we use music-related derivative videos created by
the collaboration of two or more creators on Niconico. On
Niconico, derivative creation activity is very active. As ofJanuary 2018, more than 630,000 derivative videos had been
uploaded to Niconico. In derivative videos, creators give awide variety of performances such as covering a song, dancing to music, and playing a song on a musical instrument.
Since Niconico does not provide collaboration data (e.g., theset of creators who collaborated to create a video), we collect the data as follows. Niconico users can make a video list
to list their favorite videos, and a user can see the video listsof other users. Creators often make video lists that consist ofvideos created by the creator. We call such a video list a work
list. To judge whether each list is a work list, we use severalrules. For example, given a creator’s video list, we check if
the creator’s name appears in the title, tags, or description ofeach video; if 90% or more videos in the list satisfy this condition, we treat the list as the work list. If a video is included3

http://songrium.jp

Percentage ofcollaborative videos

2520
1510502008 2009 2010 2011 2012 2013 2014 2015 2016
Month posted
Figure 1: Percentage of collaborative videos per month.
Number of collaborative videos

number of his/her publications. With the increasing popularity of online creative collaborations on web services, researchers started to analyze the collaboration activity by using large-scale data obtained from the services. These studies mainly focused on collaboration success where successis defined as completing a collaboration and releasing a finished work (Luther and Bruckman 2008). They have revealed the principles that lead to the success of collaborations (Luther et al. 2010; Settles and Dow 2013). Our study
is different from theirs in that we analyze successfully created content as a result of the collaboration.
As the progressiveness and importance of N-th orderderivative creation has become recognized (Goto 2012),researchers have worked in this research area from various aspects. Hamasaki, Takeda, and Nishimura (Hamasaki,Takeda, and Nishimura 2008) analyzed the relationships between an original work and its derivative works on Niconico. They reported several statistics such as the number ofderivative works created from an original work. Based on
the analysis, a web service called Songrium3 was developed
to help a user browse original songs and their derivativeworks by visualizing the relations between them (Hamasakiand Goto 2013). Tsukuda, Hamasaki, and Goto (Tsukuda,Hamasaki, and Goto 2016) proposed a probabilistic model
for inferring factors that triggered derivative content creation. Several studies tried to reveal the characteristics ofcontent that is more likely to be used as source content
to create new content (Hill and Monroy-Hernández 2012;
Calefato, Iaffaldano, and Lanubile 2018). For example, Hilland Monroy-Hernández (Hill and Monroy-Hernández 2012)
reported that content with low similarity to other content
tends to be used more often to create derivative content. Our
study sheds new light on this research field by analyzing thecollaboration in N-th order derivative creation in terms ofcontent popularity and creator activity.

105104103102101100
51015202530
Number of collaborative creators of video

35

Figure 2: Distribution of the number of participating creatorsof each collaborative video.
in two or more creators’ work lists, we regard the video asa collaborative video. By following this process, we collect
collaborative videos from all creators’ work lists.
We used the data of derivative videos, creators, and their
video lists provided by Hamasaki and Goto (Hamasaki andGoto 2013). The derivative videos were uploaded to Niconico between September 2007 and February 2016. The aforementioned work list detecting process gave us 270,814 work
lists from 515,297 video lists. The work lists had 363,338
unique derivative videos; among them, 83,496 videos weredetected as collaborative videos4 .

3.2

Basic Statistics

In the work lists, the number of derivative videos that werecreated by one creator (i.e., the number of non-collaborativevideos) is 279,842. That is, the percentage of collaborative
videos is 23.0%. Figure 1 shows the percentage of collaborative videos for each month. The percentage increases when
the N-th order derivative creation activity became populararound 2008. Since the percentage stays at around 20% after
2009, we can say that collaboration has occurred at a certain
level in recent years. In terms of creators’ collaboration experience, 22,841 creators among the 46,511 creators in thedataset (i.e., 49.1%) have created at least one collaborative
video. Finally, Figure 2 shows the distribution of the numberof participating creators of each collaborative video. It canbe observed that collaboration between two or three creators
is quite common: 77.3% (16.7%) of the collaborative videos
4Since some of the collected collaborative videos were createdby an unusually large number of creators, we manually checked 15videos in which 20 or more creators collaborated and removed 9videos that were wrongly judged as collaborative ones.

500

80400

60300

40200

201000100101102104103
View count1051060107

400
300
200
1000Figure 3: Distributions of view count for collaborative
videos and non-collaborative ones.

4Video Popularity

For creators, the popularity of their videos on the video sharing service is an important factor because a creator will be
motivated to create new videos if his/her videos have become popular. In this section, we regard the video’s viewcount as a measure of its popularity and analyze the effectof collaboration on the popularity.

4.1

Video-based Analysis

First, to answer the research question “is there a difference in popularity between collaborative videos and noncollaborative ones?”, we compare their view counts. The results are shown in Figure 3 where each blue (orange) dot
represents the number of collaborative (non-collaborative)
videos whose view count is x. The peak of collaborative
videos is further to the right than that of non-collaborative
ones. In addition, the median of each group, which is represented by vertical dotted lines, shows the same result. These
results indicate that there is a difference in popularity, andcollaborative videos tend to become more popular than noncollaborative ones. We presume that when multiple creators
collaborate and create a video, the fans of each creator watch
the video; this results in an increase of the view count compared to that of non-collaborative videos.

4.2

Creator-based Analysis

Our next research question is “is there a difference in popularity between creators with collaboration experience andones without it?” To answer the question, we compare theview count of non-collaborative videos between experienced
creators and inexperienced ones. To be more specific, foreach creator, given all of his/her non-collaborative videos,
we compute the median of their view counts5 . Figure 4
shows a histogram where each bar represents the numberof creators whose median view count is x. Since both thepeaks and the median values of creators with collaboration
5In this analysis, creators who have created only collaborative
videos are eliminated.

101

102

105
104
103
Median view count

106

107

Figure 4: Distributions of median view count for creatorswith collaboration experience and inexperienced ones.
Median of median view counts

were created by collaborations between two (three) creators.
The collaborative video with the largest number of participants was created by the collaboration of 34 creators. In thevideo, a creator sings a song with the other 33 creators incelebration of the second anniversary of the creator creating
content on the video sharing service.

Experienced (n = 22,841)
Inexperienced (n = 23,670)

500
Number of creators

100

Number of non-collaborative videos

Number of collaborative videos

700
Collaborative videos (n=83,496)
Non-collaborative videos (n=279,842) 600

120

1400
1200
1000
800
600
400

0255075 100 125 150 175
Number of collaborative videos posted

200

Figure 5: Relationship between the number of collaborative
videos for each creator and their view counts.
experience are further to the right than those of inexperienced ones, we can say that videos created by experienced
creators tend to be more popular. However, it is not clear if
the videos created by experienced creators are popular regardless of their experience or the videos became popular
as a result of their experience. To answer this question, periodically collecting videos’ view counts and evaluating thetransition of their view counts are required; we leave this as
future work.

4.3

Collaboration-frequency-based Analysis

Finally, we answer the research question “is there a difference in popularity between creators who have a lot of collaboration experience and those who have a little experience?”by analyzing the relationship between the number of a creator’s collaborative videos and his/her view counts. Givencreators who have created x or more collaborative videos,
we compute the median value of each creator’s median viewcount. Figure 5 shows the results where x ranges from 1 to200. It can be observed that creators who have created morecollaborative videos tend to have a higher view count. If weregard a creator’s median view count as his/her popularity,
we can say that the more collaborative videos he/she creates,
the higher his/her popularity becomes.

5Creator Activity

For video sharing services, it is desirable that creators act(i.e., create content) for a long time because it leads to thecontinuous growth of the service. In this section, we analyze
creators’ activity in terms of the continuity of collaborationand the active period.

100

100.0

Cumulative percentageof creators

Percentage of creator pairs

1008060
40

25.7
200

12.9
12

7.9

5.4

4.0 3.03456
7Number of collaboration times

2.38

1.8
9

Figure 6: Percentage of creator pairs according to collaboration times.

5.1

Collaboration Continuity

Our first research question is “once a creator collaborates
with another creator, do they continuously collaborate andkeep creating videos?” To answer this question, we analyze how often each creator collaborates with the same creator. Given a creator, we collect all creators who have collaborated with him/her; we then compute the percentageof creators who have collaborated with him/her x or moretimes. Figure 6 shows the results for all creators. The number of pairs of creators who collaborated at least one timewas 192,374. Among them, 25.7% pairs collaborated two or
more times, and 5.4% pairs collaborated as many as five or
more times. These results indicate that collaboration is not a
one-off activity; rather, it is continuous at a certain level.

5.2

Active Period

Our next research question is “is there a difference in the active period between creators with collaboration experienceand inexperienced ones?” To answer this question, we compare the active period for both creator groups. We define theactive period of a creator as the period between the posted
date of his/her first video and that of his/her latest one. Sincea creator who posted his/her first video earlier tends to havea longer active period, we group creators according to theyear of their first posted date and compute the active period for each group. Figure 7 shows the results of creators
who posted their first video in 2012. In terms of the percentage of creators whose active period is x or fewer days,
the percentage of creators with collaboration experience isalways lower than that of inexperienced creators. This result means that creators with collaboration experience tend
to have a longer active period than inexperienced ones. Similar results were observed in other years. Thus, we can conclude that collaboration experience has a relation with creators’ active periods. At the moment, we cannot determine
whether a creator’s active period becomes long as a result of
his/her collaboration experience. However, if we can show acausal correlation in our future work, supporting creators sothat they can collaborate with others more easily would be
useful to realize their continuous activity.

6Conclusion

In this paper, we focused on the N-th order derivative creation and analyzed the collaboration of music-related deriva-

80
6040
Experienced (n = 4,756)
Inexperienced (n = 4,431)

2000200

400

600
800 1000
Active period (days)

1200

1400

Figure 7: Distributions of active periods for creators withcollaboration experience and inexperienced ones in 2012.
tive videos in terms of video popularity and creator activity. Our analysis results showed the positive correlation ofcollaboration with both of them. For future work, we plan
to conduct more elaborate studies on the collaboration byconsidering each creator’s characteristics and the contentof each collaborative video. This would enable us to get adeeper understanding of collaboration such as the reasons
why creators collaborate.

Acknowledgments
This work was supported in part by JSPS KAKENHI Grant Number 17K12688 and JST ACCEL Grant Number JPMJAC1602,Japan.

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