Case study 02 · Graduate research · 2025
Analysis of
Popular Songs
in the US Market.
What makes a song popular—and can the shape of that popularity be predicted?
The question
Popularity is more than what a song sounds like. It is also a pattern of human attention unfolding over time.
The research examined both the descriptive and predictive dimensions of song popularity: musical structure, lyrical sentiment, platform engagement, and weekly consumption behavior.
The dataset
One question.
Multiple signals.
Charts
Billboard Hot 100 rankings and genre classifications.
Sound
Spotify acoustic features and popularity scores.
Behavior
Streaming, airplay, and sales metrics from Luminate Music Connect.
Language
Lyrics and sentiment analysis from a selected subset.
The methods
Prediction,
classification,
and pattern.
Random Forest and XGBoost models were used alongside Principal Component Analysis and K-Means clustering. TSFresh extracted hundreds of features from weekly streaming histories to examine how consumption changed over time—not simply where it ended.
Prediction accuracy using the combined feature set
The strongest view came from combining what a song is with what listeners do.
Selected findings
Behavior carries
the signal.
Time matters
Streaming volatility and structured growth patterns offered meaningful predictive power.
Audio streaming matters
On-demand audio streaming contributed significantly to predicting popularity.
More data is not always better
Streamed video added little predictive value, suggesting it could be removed to make classification more efficient.
Genre was not destiny
Genre was not useful for predicting user engagement, despite its value as a familiar organizing label.
The contribution
A hybrid framework for understanding popularity through both content and behavior—with practical implications for recommendation, marketing, and artist development.
The complete work
Read the thesis.
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