Song popularity prediction by the Complexity Science Hub © yvette-de-wit-unsplash

11.06.2024

News

The power of social connections to predict hit songs

The artist’s fame or genre influence aren’t the only factors that matter, say Complexity Science Hub researchers

Ever wondered how your friends shape your music taste? In a recent study, researchers at the Complexity Science Hub (CSH) demonstrated that social networks are a powerful predictor of a song’s future popularity. By analyzing friendships and listening habits, they’ve boosted machine learning prediction precision by 50%.

"On Spotify alone, more music is released in a year than we could listen to in our entire lives"

“Our findings suggest that the social element is as crucial in music spread as the artist’s fame or genre influence,” says Niklas Reisz from CSH. By using information about listener social networks,  along with common measures used in hit song prediction, such as how well-known the artist is and how popular the genre is, the researchers improved the precision of predicting hit songs from 14% to 21%. The study, published in Scientific Reports, underscores the power of social connections in music trends.

A DEEP DIVE INTO DATA

The CSH team analyzed data from the music platform last.fm, analyzing 2.7 million users, 10 million songs, and 300 million plays. With users able to friend each other and share music preferences, the researchers gained anonymized insights into who listens to what and who influences whom, according to Reisz.

For their model, the researchers constructed two networks: one mapping friendships and another capturing influence dynamics — who listens to a song and who follows suit. “Here, the nodes of the network are also people, but the connections arise when one person listens to a song and shortly afterwards another person listens to the same song for the first time,” explains Stefan Thurner from CSH.

Examining the first 200 plays of a new song, they predicted its chances of becoming a hit — defined as being in the top 1% most played songs on last.fm.

New study shows the power of social connections to predict hit songs © Complexity Science Hub
(a+b) If a user listens to a song and a friend of that user listens to the same song for the first time, the second user is said to have been influenced by the first with respect to that song. The influencer network (orange) is a sub-network of the friendship network (blue). In the network several strong influencers are visible (influencers and influencing links in orange) (c) Example of a timeline of a single song.

USER INFLUENCE

The study found that a song’s spread hinges on user influence within their social network. Individuals with a strong influence and large, interconnected friend circles accelerate a song’s popularity. According to the study, information about social networks and the dynamics of social influence enable much more precise predictions as to whether a song will be a hit or not.

“Our results also show how influence flows both ways — people who influence their friends are also influenced by them” explains CSH researcher Vito D.P. Servedio. “In this way, multi-level cascades can develop within a very short time, in which a song can quickly reach many other people, starting with just a few people.”

SOCIAL POWER IN THE MUSIC INDUSTRY

Predicting hit songs is crucial for the music industry, offering a competitive edge. Existing models often focus on artist fame and listening metrics, but the CSH study highlights the overlooked social aspect — musical homophily, which is the tendency for friends to listen to similar music. “It was particularly interesting for us to see that the social aspect, musical homophily, has so far received very little attention – even though music has always had a strong social aspect,” says Reisz.

The study quantifies this social influence, providing insights that extend beyond music to areas like political opinion and climate change attitudes, according to Thurner.

About the study

The study “Quantifying the impact of homophily and influencer networks on song popularity prediction,” N. Reisz, V. D. P. Servedio, and S. Thurner, is available in Scientific Reports (doi: 10.1038/s41598-024-58969-w).

Researchers

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