Measuring cultural value using social network analysis: a case study on valuing electronic musicians

Measuring cultural value using social network analysis

A case study on valuing electronic musicians

Anna Jordanous (University of Kent, CC/music informatics), Daniel Allington (University of the West of England, SNA/digital

culture), Byron Dueck (Open University, music/ethnomusicology)

Valuing Electronic Music http://valuingelectronicmusic.org

http://valuingelectronicmusic.org/

Types of value


• Monetary value £ $ € – (what a thing is worth)

• “Good” things – e.g. “good” opinions – (Thanks Mike Cook)

• etc …

• Cultural value– Based on what we are culturally led to like– Subjective– Value shared between members of a community/culture

Introduction: Aim of this work

Can we measure the cultural value of a creative entity in a quantitative / algorithmic way?

In other words… Is there a way to judge cultural value, that can be computerised and stuck into the creative

process of a computational creativity system?NB Evaluation is an important part of the

creative process… but this is another talk…


Evaluating value (as part of evaluating creativity)

We (CC) would like to have metrics for value that we can implement

computationally

Q How do we measure/assess cultural value?…using social network analysis?


Some premises before we start

1. Evaluating value is an important part of evaluating how creative something is

2. If you want to know how good a creative entity is, ask someone (something) that knows what it means to be good in that way

3. This is a case study in judging value of electronic musicians, but can be useful for other creative domains (with a little work)


Premise 1. Evaluating value is an important part of evaluating how creative something is

Creativity = novelty + value + …??


Premise 2. To know how valuable a creative entity is, ask someone/something(s) that knows that creative area


https://www.youtube.com/watch?v=FZu097wb8wU



e.g. Would you trust Dan Ventura on what art galleries are good in Park City?


Premise 3: Our approach to measuring value in electronic music can be useful more widely

• ‘One size does not fit all’

• But [I argue] our approach is useful, if you want to analyse cultural value in domains other than electronic music

• (with a little work)


Premises set out… let’s begin

We (CC) would like to have metrics for value

Q How do we measure/assess cultural value?…using social network analysis?

Case study: electronic music[ians]

– Who are the main ‘players’ (no pun intended..?)

– Who values who? How do they show this?


Qualitative and Quantitative work informing each other

Interviews SoundCloud data analysis


e.g. focus on valuing (as a verb), not value (as a noun)

e.g. it is possible to create SC groups - is this useful?

Distinct types of electronic music


http://valuingelectronicmusic.org/2014/12/19/glitch-lich-live-performance/

http://valuingelectronicmusic.org/2015/01/02/winterlight-live-set/

http://valuingelectronicmusic.org/2015/02/01/slackk-live-set/

http://valuingelectronicmusic.org/2014/11/25/slackk-winterlight-glitch-lich-electronic-music-producers-panel/











(Where we left this - work-in-progress paper ICCC’14)


Little interest in the ‘top x users’ across the whole groupMore important: Relationships / personal networks

smal ler networks of usersinfl uenced by genresgeographica l ly infl uenced

Who is on SoundCloud?

From analysing ‘follow’ relationships:–By countries, top 3 are US, UK, Germany–By cities, top 3 are London, New York, Berlin–People tend to follow people within same city–http://valuingelectronicmusic.org/2014/09/08/geography-soundcloud-following/

• And people in same genre

–By genre, top 3 are house, hip hop, techno–http://valuingelectronicmusic.org/2014/06/04/exploring-genre-on-soundcloud-part-i/–NB This is not filtered for electronic music genres - but electronic music dominates


http://valuingelectronicmusic.org/2014/09/08/geography-soundcloud-following/



http://valuingelectronicmusic.org/2014/06/04/exploring-genre-on-soundcloud-part-i/




Comments: typically very positive


Comments highlighted by interviewees

as an important valuing activity


Identifying sub-networks: place

• Ego-network of one of our interviewees and their immediate followers/followees

• Colours = places– London = Green– Bristol = Purple– LA = Red

(Tony what should we call these colours?)



Identifying sub-networks: genres

• Three distinct macro-genres identified: – EDM– Urban– ‘other’

• (Corresponds with music research groupings)



Distinct vocabularies per genres

# Techno Dubstep House Hiphop

1 set sick nice dope

2 great tune house shit

3 tracks nice super beat

4 loved big production leave

5 fantastic mix support song


Top words appearing in comments per genre


Bringing this back to computational creativity - how can CC use this work?

As a proxy for value:– look for [meaningful]

interactions between people linked together in subnetworks / ego networks / cliques= evidence of interpersonal relationships

– On SoundCloud, interactions tend to be (or spam)


For a ‘CC electronic musician’:– Set up London-based SC

account + upload tracks– Develop the system to

interact with other music-makers in similar genres: • Comment on other tracks and

respond to comments using that genre’s key vocab• Follow users in those genres

– Follow users in key cities (e.g London, NY, Berlin, LA)


Remember: comments usually positive


Comments highlighted by interviewees

as an important valuing activity


Hmm.. Can commenting be used as a simple proxy for value judgments?

• Using commenting as a valuing activity• # chars track comments = proxy value metric?

EXPERIMENTING: Trying this on Scottish users in our data1. calvinharris (electro-pop, house)2. the-nibelheim-incident (Daryl Constance) (dubstep)3. === middleschoolfrown (John Kevin, electronic pop),

davecruickshank (techno), jasegallacher (techno)

• But differences in commenting behaviour by genre (not just based on the different vocabulary used)

• dubstep producers most prolific (2569 comments)

• then techno (2254), hiphop (2081) and house (1725)Valuing Electronic Music http://valuingelectronicmusic.org

Early days … work in

progress


Conclusions

• You can ‘proxy’ cultural value computationally by studying social network activity– But – look at smaller subnetworks, not the whole– In Soundcloud: networks around place and genre

• How do people show they value others’ work? – Look for relationships/links between people– Work out what linking activities indicate more value

• In Electronic Music it is commenting and collaborating

• We could take advantage of this to measure value computationally, as part of evaluating creativity



Documents

Measuring cultural value using social network analysis: a case study on valuing electronic musicians