Speaker
Description
The widespread adoption of digital music streaming platforms has created unprecedented opportunities to analyze large-scale music consumption data. This study investigates global music trends by analyzing Spotify track data using statistical and visualization techniques implemented in R. The objective is to explore how various audio features—including danceability, energy, valence, acousticness, and tempo—relate to song popularity and how these characteristics have evolved across different release periods.
The analysis utilizes a publicly available Spotify tracks dataset and follows a structured data analytics workflow consisting of data preprocessing, exploratory data analysis, correlation analysis, and feature-based trend visualization. Using R packages such as tidyverse, ggplot2, and dplyr, the study examines relationships between musical attributes and listener engagement while identifying patterns that characterize highly popular tracks.
Visual analytics techniques are employed to reveal temporal changes in audio feature distributions and highlight potential associations between musical structure and audience preferences. Preliminary findings suggest that certain combinations of audio attributes—particularly higher energy and danceability levels—are frequently associated with increased popularity, indicating evolving stylistic preferences in contemporary music production.
This work demonstrates the effectiveness of R as a flexible platform for large-scale music data analysis and illustrates how data mining and visualization methods can provide valuable insights into modern music consumption patterns. The findings contribute to a broader understanding of how data-driven approaches can be applied to analyze trends in the digital music ecosystem.
If you used AI tools or services to support the preparation of this submission, please state the name and reason for using each of them.
ChatGPT was used to assist in improving the clarity, grammar, and structure of the abstract and LaTeX formatting. All ideas, project design, and final content decisions were made by the author.
Additional Material or Paper
This work has not been previously published. The project repository containing the dataset references and R analysis scripts will be available on GitHub.
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | Spotify Data Analysis, Music Trend Analysis, Data Visualization, Audio Feature Analysis, R Programming |
|---|---|
| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
| Material License | CC-BY 4.0 |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |
| Interested in serving as reviewer? | yshristi1920@gmail.com |