Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018)

Collaboration in N-th Order Derivative Creation
Shiori Hironaka,1 Kosetsu Tsukuda,2 Masahiro Hamasaki,2 Masataka Goto,212Toyohashi 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

some studies about N-th order derivative creation such as analyzing the citation relationships between content (Hamasaki,Takeda, and Nishimura 2008) and detecting characteristics
of content that is more likely to be used as source content
to create new content (Hill and Monroy-Hernández 2012;
Calefato, Iaffaldano, and Lanubile 2018). However, to thebest of our knowledge, no study has analyzed the collaboration in N-th order derivative creation in terms 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.

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 thispaper, we analyze the collaboration of music-related derivative
videos on a video sharing service. Speciﬁcally, by using 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 showedthat collaborative videos tend to become more popular thannon-collaborative ones, the collaboration is not a one-off activity but a continuous one, and creators who have collaboration
experience are active for a longer time than inexperienced
creators, etc.

1

Introduction• Video popularity: for a creator, the popularity of his/her
videos is important because he/she will be motivated to create new videos if his/her videos have become popular on avideo sharing service. Hence, we analyze the collaborationin terms of video popularity. (Section 4)

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 from scratch,
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 a cover 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 derivative creation (Goto2012). As we will show in Section 3.2, collaborations between creators to create content in N-th order derivative creation are also common. In such content, for example, multiple
creators sing a song together or one creator plays 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 isimportant from the social scientiﬁc viewpoint, several studies have analyzed the collaboration activities to create content (Hu, Chen, and Luan 2014; Luther et al. 2010). There are• Creator activity: for video sharing services, it is important that 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 ofcreators’ 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 inexperienced creators.

2Related Work

Studies dealing with collaboration in human society havebeen 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 the number of his/herc 2018, Association for the Advancement of Artiﬁcial
Copyright Intelligence (www.aaai.org). All rights reserved.
1
https://www.youtube.com/

2608

http://www.nicovideo.jp

publications. With the increasing popularity of online creative collaborations on web services, researchers started toanalyze the collaboration activity by using large-scale dataobtained from the services. These studies mainly focusedon collaboration success where success is deﬁned as completing a collaboration and releasing a ﬁnished work (Lutherand Bruckman 2008). They have revealed the principles thatlead to the success of collaborations (Luther et al. 2010;Settles and Dow 2013). Our study is different from theirs inthat we analyze successfully created content as a result of thecollaboration.
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 anoriginal work and its derivative works on Niconico. They
reported several statistics such as the number of derivative works created from an original work. Based on theanalysis, 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.

33.1

Figure 1: Percentage of collaborative videos per month.

Figure 2: Distribution of the number of participating creatorsof each collaborative video.
we treat the list as the work list. If a video is included in
two or more creators’ work lists, we regard the video as acollaborative 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 were detected
as collaborative videos4 .

Dataset3.2

Development

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
were created by collaborations between two (three) creators.

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 of January 2018,
more than 630,000 derivative videos had been uploaded toNiconico. In derivative videos, creators give a wide varietyof 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., the set of creators
who collaborated to create a video), we collect the data asfollows. Niconico users can make a video list to list theirfavorite videos, and a user can see the video lists of otherusers. Creators often make video lists that consist of videos
created by the creator. We call such a video list a work list.
To judge whether each list is a work list, we use several rules.
For example, given a creator’s video list, we check if the creator’s name appears in the title, tags, or description of each
video; if 90% or more videos in the list satisfy this condition,
3

Basic Statistics4
Since some of the collected collaborative videos were createdby an unusually large number of creators, we manually checked 15
videos in which 20 or more creators collaborated and removed 9
videos that were wrongly judged as collaborative ones.

http://songrium.jp609

Figure 3: Distributions of view count for collaborative videos
and non-collaborative ones.

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

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.

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 effect ofcollaboration on the popularity.

4.1

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

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

4.3

Collaboration-frequency-based Analysis

Finally, we answer the research question “is there a differencein popularity between creators who have a lot of collaboration experience and those who have a little experience?” byanalyzing the relationship between the number of a creator’s
collaborative videos and his/her view counts. Given creators
who have created x or more collaborative videos, we compute the median value of each creator’s median view count.
Figure 5 shows the results where x ranges from 1 to 200. It
can be observed that creators who have created more collaborative videos tend to have a higher view count. If we regard acreator’s median view count as his/her popularity, we can saythat the more collaborative videos he/she creates, the higher
his/her popularity becomes.

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, wecompute the median of their view counts5 . Figure 4 shows ahistogram where each bar represents the number of creators
whose median view count is x. Since both the peaks and themedian values of creators with collaboration experience arefurther to the right than those of inexperienced ones, we can5

Creator 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.

5.1

5Collaboration Continuity

Our ﬁrst research question is “once a creator collaborates
with another creator, do they continuously collaborate andIn this analysis, creators who have created only collaborative
videos are eliminated.

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Figure 7: Distributions of active periods for creators withcollaboration experience and inexperienced ones in 2012.

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

each creator’s characteristics and the content of each collaborative video. This would enable us to get a deeper understanding of collaboration such as the reasons why creators
collaborate.

keep 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 percentage of creators
who have collaborated with him/her x or more times. Figure 6 shows the results for all creators. The number of pairsof creators who collaborated at least one time was 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

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

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Active Period

Our next research question is “is there a difference in theactive 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 whoposted their ﬁrst video in 2012. In terms of the percentage ofcreators whose active period is x or fewer days, the percentage of creators with collaboration experience is always lower
than that of inexperienced creators. This result means thatcreators with collaboration experience tend to have a longer
active period than inexperienced ones. Similar results wereobserved 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 a causal correlation in
our future work, supporting creators so that they can collaborate with others more easily would be useful to realize theircontinuous activity.

6Conclusion

In this paper, we focused on the N-th order derivative creation
and analyzed the collaboration of music-related derivative
videos in terms of video popularity and creator activity. Our
analysis results showed the positive correlation of collaboration with both of them. For future work, we plan to conduct
more elaborate studies on the collaboration by considering

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