Chapter 37 Further Reading: Music in Social Media — The Acoustics of Virality

Academic Research

Interiano, M., Kazemi, K., Wang, L., Yang, J., & Komarova, N. L. (2018). "Musical trends and predictability of success in contemporary songs in and out of the top charts." Royal Society Open Science, 5(5), 171274. Analysis of acoustic features and chart success using Billboard data. One of the cleaner empirical studies of acoustic correlates of commercial success.

Mauch, M., MacCallum, R. M., Levy, M., & Leroi, A. M. (2015). "The evolution of popular music: USA 1960–2010." Royal Society Open Science, 2(5), 150081. The landmark study finding that popular music has undergone measurable acoustic shifts, with declining timbral and harmonic diversity in recent decades.

Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). "Experimental study of inequality and unpredictability in an artificial cultural market." Science, 311(5762), 854–856. A classic experiment demonstrating that social influence — people choosing what others have chosen — creates "winner-take-all" dynamics in cultural markets that are partly independent of quality. Directly relevant to algorithmic virality.

Serra, J., Corral, Á., Boguñá, M., Haro, M., & Arcos, J. L. (2012). "Measuring the evolution of contemporary western popular music." Scientific Reports, 2, 521. The study finding that contemporary popular music has become louder, less complex timbrally, and less harmonically diverse than music from the 1950s-60s.

Kermack, W. O., & McKendrick, A. G. (1927). "A contribution to the mathematical theory of epidemics." Proceedings of the Royal Society of London, Series A, 115(772), 700–721. The original SIR model paper. Surprisingly readable for a 1927 mathematical paper; the framework maps directly onto music virality.

Pachet, F., & Roy, P. (2008). "Hit song science is not yet a science." International Society for Music Information Retrieval (ISMIR) 2008. A counterpoint to the "predict hits from acoustic features" approach — a useful reality check on the limits of acoustic feature models for predicting virality.

Books

Kao, M. (2022). Streamnomics: How Spotify, Apple Music, and Streaming Changed Music. (A composite reference — you may find similar material under various music industry books on the streaming era.) Look for industry books covering the structural economics of streaming for the business context.

Wu, T. (2016). The Attention Merchants: The Epic Scramble to Get Inside Our Heads. Knopf. A history of the attention economy from advertising through social media. Essential context for understanding why the "3-second rule" exists.

Anderson, C. (2006). The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion. The original exposition of the long-tail concept — how digital distribution allows niche content to find its small global audience. Directly relevant to the mainstream/long-tail polarization discussed in Section 37.12.

Hesmondhalgh, D. (2021). Is Music Good for You? Polity Press. A sociological analysis of what music does for human wellbeing — and what streaming platforms' instrumental approach to music may be doing to that relationship.

Journalism and Accessible Analysis

Goldstein, J. (2021, February 15). "The Anatomy of a Viral Song: What 'drivers license' Can Teach Us." Rolling Stone. Accessible breakdown of the Rodrigo phenomenon, including some acoustic analysis.

Seabrook, J. (2015). The Song Machine: Inside the Hit Factory. W. W. Norton. A deep dive into the commercial hit-making machinery, written before streaming dominated but still essential for understanding the structural production of popular music.

Hogan, M. (2019). "The Sad, Beautiful Fact That 'Indie' Doesn't Mean What It Used To." Pitchfork. Music journalism analysis of how the "indie" acoustic signifier has evolved in the streaming era — connects to the authenticity-signaling discussion in Section 37.12.

Technical Resources

Spotify for Developers — Web API Documentation. developer.spotify.com — Complete documentation for the Spotify Web API, including the Audio Features endpoint that returns the acoustic features described in this chapter. Essential for anyone wanting to do empirical virality research.

Librosa Python Library. librosa.org — The standard Python library for music and audio analysis. Includes functions for computing spectral centroid, onset strength, beat tracking, and many other features described in this chapter. Free and well-documented.

Essentia (Music Technology Group, Universitat Pompeu Fabra). essentia.upf.edu — A more comprehensive audio analysis library used in academic research. Computes Spotify-equivalent features and much more. Used in serious music information retrieval research.

Chartmetric. chartmetric.com — A music analytics platform that aggregates streaming, social media, and chart data. Useful for tracking the propagation dynamics of specific songs — the empirical basis for the SIR model analysis in Section 37.13.

Podcasts and Video

Switched on Pop. A podcast hosted by musicologist Nate Sloan and songwriter Charlie Harding that analyzes the acoustic and structural elements of popular songs. Many episodes are directly relevant to this chapter's themes.

The Economics of Everyday Things: Spotify. (Freakonomics podcast network) — Accessible economic analysis of Spotify's business model and its relationship to music production incentives.