Date of Graduation
5-2023
Document Type
Thesis
Degree Name
Bachelor of Science in Industrial Engineering
Degree Level
Undergraduate
Department
Industrial Engineering
Advisor/Mentor
Chimka, Justin
Committee Member/Reader
Liu, Xiao
Abstract
Understanding music popularity and what drives it is important not only for artists but for other individuals who are financially tied to music sales including producers, writers, and record labels. Studies have been done to define how a song’s popularity can be measured, what attributes or features are drivers for popularity, and to what extent can a song’s popularity even be predicted. This paper takes two linear regression approaches to predicting the popularity of a Taylor Swift song on Spotify based on auditory features the Spotify API estimates and historic popularity of songs on Spotify. One model takes into consideration interacting predictors to divide the data into four different subsets. Another model uses backward elimination to generate one model that describes the whole dataset. Based on Bayesian Information Criteria, the collection of four models does a better job predicting a song’s popularity compared to the backward elimination model. Additionally, both models found a song’s acousticness and release year as the two most important predictors of popularity.
Keywords
linear regression, statistics, music
Citation
Schneidewind, H. (2023). Interaction Effects and Selecting Regression Models of Taylor Swift Song Popularity. Industrial Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/ineguht/87