Soviet Popular Music Landscape: Community Structure and Success Predictors

100 %
0 %
Information about Soviet Popular Music Landscape: Community Structure and Success Predictors

Published on June 29, 2016

Author: DmitryZinoviev

Source: slideshare.net

1. Soviet Popular Music Landscape Community Structure and Success Predictors Dmitry Zinoviev Department of Mathematics and Computer Science Suffolk University, Boston

2. Dmitry Zinoviev * IC S * Suffolk University  2 Research Question Who Rocks and Why?

3. Dmitry Zinoviev * IC S * Suffolk University  3 Real Research Questions ● Does sharing performers with other groups influence the groups' eventual success? ● If so, is the success predictable from the performers' sharing network? ● What is the linguocultural and genre structure of the ex-Soviet music universe?

4. Dmitry Zinoviev * IC S * Suffolk University  4 Research Strategy ● Collect data about sharing and success ● Build a network based on shared musicians ● Define “success” ● Correlate network measures (such as centralities) with success measures ● Attempt to predict success from the network measures using machine learning techniques ● Look into genres/languages and communities

5. Dmitry Zinoviev * IC S * Suffolk University  5 DATA

6. Dmitry Zinoviev * IC S * Suffolk University  6 Data Set ● 4,560 non-academic music groups performing in the USSR and post-Soviet countries in 1960–2015 ● 17,000 performers (at least 3,600 shared) ● 275 coded genres (rock, pop, disco, jazz, folk, etc.) ● Wikipedia pages in 122 languages

7. Dmitry Zinoviev * IC S * Suffolk University  7 New Groups by Year

8. Dmitry Zinoviev * IC S * Suffolk University  8 2,216 Groups on Wikipedia ● Russia ● Estonia ● Ukraine ● Latvia ● Lithuania ● Belarus ● Moldova

9. Dmitry Zinoviev * IC S * Suffolk University  9 NETWORK

10. Dmitry Zinoviev * IC S * Suffolk University  10 Network Construction ● Group → node; labels in the original language ● Two nodes connected if the groups shared at least one musician over their lifetime ● Undirected, unweighted, unconnected graph with no loops and no parallel edges ● For each node, calculate degree, average neighbors degree, closeness, betweenness, and eigenvalue centrality, and clustering coefficient

11. Dmitry Zinoviev * IC S * Suffolk University  11 Network Overview ● Node size represents degree (number of shares)

12. Dmitry Zinoviev * IC S * Suffolk University  12 Network Description ● 80% of the groups (3,602) are in the giant connected component; all other connected components have <13 groups each ● Excellent community structure (m=0.76), 43 communities; each of the largest 25 communities has 20+ groups ● Community = groups that have a lot of mutual musician sharing

13. Dmitry Zinoviev * IC S * Suffolk University  13 SUCCESS

14. Dmitry Zinoviev * IC S * Suffolk University  14 What's “Success”? ● No sales data! ● No charts! ● Informal/semi-legal/illegal status ● Proxies for long-term success (we still remember them!): – Wikipedia page(s) visit frequency within last 3 years (collected from http://stats.grok.se) – Wikipedia page(s) Google PageRank – Available for 2,000 groups

15. Dmitry Zinoviev * IC S * Suffolk University  15 PageRank (PR) Correlations

16. Dmitry Zinoviev * IC S * Suffolk University  16 Visit Frequency (VF) Correlations

17. Dmitry Zinoviev * IC S * Suffolk University  17 Prediction (1) ● Random Decision Forest (RDF) machine learning predictor ● Predict above-median VF vs below-median VF: accuracy 69% (expected by chance: 50%) ● Predict Google PR: accuracy 50% (expected by chance: 17%); 95% if 1 error allowed ● Quite poor, but not hopeless

18. Dmitry Zinoviev * IC S * Suffolk University  18 Prediction (2) ● But isn't visit frequency affected by group size? (More performers—more search queries?) ● Add group size as a control variable ● Predict above-median VF vs below-median VF: accuracy 69% (was: 69%) ● No difference!

19. Dmitry Zinoviev * IC S * Suffolk University  19 GENRES

20. Dmitry Zinoviev * IC S * Suffolk University  20 Genres and Sharing ● Build a network of similar genres (recursive generalized similarity): – Two genres are similar if used by similar groups – Two groups are similar if play similar genres ● Genre → node; two nodes are connected if the genres are “very similar” ● Community structure (m=0.3): – Punk/jazz, metal, disco/pop, blues/hip-hop, light rock

21. Dmitry Zinoviev * IC S * Suffolk University  21 Genre Network Metal Light rock Punk Soul Folk/jazz/hh Disco Ethno Some genres are hierarchical (rock/metal/black metal). TODO: Assign them to different levels.

22. Dmitry Zinoviev * IC S * Suffolk University  22 Musicians Prefer Similar Genres

23. Dmitry Zinoviev * IC S * Suffolk University  23 LINGUOCULTURAL STRUCTURE

24. Dmitry Zinoviev * IC S * Suffolk University  24 Languages, Genres, and Sharing ● Group sharing network has 25 communities with 20+ groups in each ● Preferred language = language of the most frequently visited Wikipedia page ● Look into genres and preferred languages within each community: Are they homo- or heterogeneous?

25. Dmitry Zinoviev * IC S * Suffolk University  25 Genres per Community In 9 communities, >50% of groups perform the one genre. In 23 communities, >50% of groups perform in no more than 2 genres. 71% of all shares— homogeneous

26. Dmitry Zinoviev * IC S * Suffolk University  26 Preferred Languages per Community In 24 communities, >50% of groups have the same preferred language! 84% of all shares —homogeneous

27. Dmitry Zinoviev * IC S * Suffolk University  27 Language and Genre Homogeneity: Either or Both? Language-defined Genre-defined Not very convincing? Mixed

28. Dmitry Zinoviev * IC S * Suffolk University  28 Conclusion ● Musician sharing networks of non-academic music groups in the USSR and post-Soviet countries have community structure inspired by preferred language and musical genre ● Centrality and clustering measures of this network are correlated with long-term success of groups in terms of popularity on Wikipedia and to some extent can serve as success predictors

29. Dmitry Zinoviev * IC S * Suffolk University  29 Dataset Available ● https://github.com/dzinoviev/sovietmusic

30. Dmitry Zinoviev * IC S * Suffolk University  30 Made in Pythonia Get your copy of “Data Science Essentials in Python” at https://pragprog.com/book/dzpyds/data-science-essentials-in-python

Add a comment

Related pages

Russia - Social Structure - Country Studies

Perhaps the most significant fact about Russia's social structure is ... The social structure of the Soviet Union was ... , music, and ...
Read more

www.learninglandscapes.ca

www.learninglandscapes.ca
Read more

Latvia - Media Landscape | European Journalism Centre (EJC)

EUROPEAN JOURNALISM CENTRE; ... which plays Latvian popular music on a 24/7 basis, ... Its detective films were particularly popular in the Soviet market.
Read more

Communist party: Introduction - Infoplease

Communist party. Introduction Communist party, in Russia and the Soviet Union, political party that until 1991 exercised all effective power within the ...
Read more

Section 10. Understanding Culture, Social Organization ...

Understanding Culture, Social Organization, and ... economic success, ... the traditional leadership structure in Middle Eastern communities tends ...
Read more

Success

... eventually leads to success. Bryan Elliott . August 10, 2016. Personal Development. Goal Setting. John C. Maxwell: 4 Ways to Reach Your Personal Best ...
Read more

The Semiotics of Music Videos, by Heidi Peeters

The Semiotics of Music Videos: It Must Be Written in ... of popular music ... Marion can help to structure the history of music video ...
Read more

Project MUSE - The Real Causes of the Color Revolutions

As in subsequent color revolutions, ... has been a surprisingly poor predictor of opposition success. ... electoral success. In the former Soviet ...
Read more

Challenges to capitalism; Russian Revolution and the ...

Challenges to capitalism; Russian Revolution and the establishment of the Communist ... The Soviet Union ... Steytlerville Community History; Popular ...
Read more

Phonological Awareness

Phonological awareness consists of skills that typically develop gradually and sequentially through the late preschool period. They are developed with ...
Read more