CSCW 2018

1. Investigating Crowd Creativity in
Online Music Communities
Fabio Calefato Giuseppe Iaffaldano Filippo Lanubile Federico Maiorano
@fcalefato @pepponefx @lanubile
Nov. 3-7, Jersey City, NJ

2. Janis
© Hanna Barbera© Hanna Barbera
© Hanna Barbera
© Hanna Barbera
Collaborative Songwriting

3. 3 Creative Music Communities
Authors 3,327
Songs 27,013
Reused 10,656
Authors 284
Songs 1,168
Reused 116
Authors 3,790
Songs 16,998
Reused 2,638

4. Songtree
Overdub
Root
(Guitar only)
Overdub
(Guitar + Voice)
Guitar + Voice
Voice
Root

5. Collaboration Models
song-tree fork-and-pull

6. GitHub vs. Songtree
Follow people, Bookmark trees, Like,
Comment songs
Collaborate through Overdub
Free author contributions
Share your own music
Follow people, Watch, Star repositories,
Comment issues
Collaborate through Fork/Pull Request
Free developer contributions
Share your own code

7. OSS Communities as Creative
Creative
communities
OSS Arts

8. 5 Hypotheses
Song-related
• H1: Songs that generate a high
amount of reactions are more
likely to be reused
• H2: More recently uploaded songs
are more likely to be reused
• H3: More derived songs are less
likely to be further reused
Author-related
• H4: Songs by authors with a high
status in the community are more
likely to be reused
• H5: Songs by authors with a
customized avatar are more likely
to be reused

9. Mixed-method Approach
Sequential explanatory strategy Steps
1. Operationalization of reuse
antecedents
2. Data collection
3. Logistic regression
4. User survey
Collection/analysis of
quantitative data
Collection/analysis of
qualitative data

10. Logistic Regression: Songtree
H1: Amount of reactions generated
H2: Recentness
H3: Generativity of derived songs
H4: Status in the community
H5: Customized avatar
SongAuthor
+++
++
+++
– –
++
Likelihood
of reuse
pseudo R2=.89
AUC=.98

11. Logistic Regression: ccMixter
H1: Amount of reactions generated
H2: Recentness
H3: Generativity of derived songs
H4: Status in the community
H5: Customized avatar
SongAuthor
+
+
–
– – –
++
Likelihood
of reuse
pseudo R2=.33
AUC=.79

12. Logistic Regression: Splice
H1: Amount of reactions generated
H2: Recentness
H3: Generativity of derived songs
H4: Status in the community
H5: Customized avatar
SongAuthor
++
+
–
– –
+
Likelihood
of reuse
pseudo R2=.19
AUC=.66

13. Questionnaire*
• 97 valid responses
• 49 from authors w/ 6+ months of experience
13
Category Q1. Triggers of
overdubbing?
Q2. Comments / requests about
collaborative features?
1 Genre Flexible genre categorization
2
Friends
network
Invitations and
@mentions
Friends
network
Virtual bands and virtual albums
3 Followings Music producers
4
Opportunity for
contributing
Better chat
5 Contests Songtree awards
6 Personal taste Abolish author ranking
* only for Songtree

14. Conclusions
What we learned from data
• Songs more likely to be reused
when …
– H1: they generate more reactions
– H3: they are less derived
– H4: their author has high status
– H5: their author has custom avatar
• Mixed evidence
– H2: likelihood of reuse for recent
songs
What we learned from authors
• More varied contests
– Best newcomer, singer, band…
– But…
• Beware of gamification side effects!
– Collaborative Competitive
songwriting songwriting
• Foster collaborations supporting
virtual bands & albums
– Support full music-production lifecycle
≠

15. Thanks from The Fab Four ;-)
collab-uniba/music_scrapers
@fcalefato
fabio.calefato@uniba.it
collab.di.uniba.it/fabio
tiny.cc/cscw18music
© Hanna Barbera
FAB
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© Hanna Barbera
GIUS
EPP
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© Hanna Barbera
FILI
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© Hanna Barbera
FED
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