Versioning, Remix Culture, and Chart Success in the Platform Era

This project examines the rise of “versioning” practices (e.g., remixes, alternate versions, and radio format-specific releases) as a central strategy for achieving and sustaining chart success in the contemporary music industry. While remixing has long been part of popular music culture, its function has shifted significantly in the platform era, particularly following major methodological changes to Billboard charts in the early 2010s. In October 2012, Billboard applied its Hot 100 methodology to its genre charts, combining digital sales, streaming, and airplay from all radio formats into a single ranking system. As outlined in previous work (Watson 2019), this shift allowed multiple versions of a song to be aggregated under a single chart entry, fundamentally altering the dynamics of chart mobility. For example, on the Hot Country Songs chart, “We Are Never Ever Getting Back Together” by Taylor Swift and “Cruise” by Florida Georgia Line were early beneficiaries of this methodological shift (10 and 24 weeks, respectively), illustrating how activity across remixes, cross-genre releases, and platform-specific versions can produce rapid chart ascents and extended runs at the top of the chart. Later songs would achieve even longer runs in the coveted #1 position. 

Building on this example, this project seeks to trace the contemporary history of remix and versioning practices within this data-driven chart environment to better understand how such strategies are used to shape chart outcomes. While these dynamics have been well documented in country music (via the Hot Country Songs chart), less work has examined how similar practices operate across other major charts, including the Hot 100, Hot R&B/Hip-Hop, Hot Laint, and Hot Rock & Alternative charts. 

Taking a data production studies approach, students will employ a data tracing methodology to document both structural changes to chart systems and the increasing use of multiple versions in the promotion of individual songs. Students will collaboratively map key shifts in chart methodology before selecting one song with multiple versions (e.g., remix releases, feature variants, acoustic or sped-up versions) to trace release histories alongside chart trajectories. Important examples include “Old Town Road” by Lil Nas X (see Chris Molanphy 2023), as well as “Savage” by Megan Thee Stallion, “Levitating” by Dua Lipa, and “Say So” by Doja Cat, each of which circulated through multiple versions that contributed to their chart success. Through this work, students will produce structured datasets that make visible how remix and versioning practices operate within contemporary platform economies. 

Objectives

1. Trace the historical development of remix and versioning practices in relation to chart systems  
2. Analyze how chart methodology shapes release strategies  
3. Document how multiple versions of songs influence chart trajectories  
4. Produce structured datasets that support analysis of platform-era music economies  

This project employs a data tracing methodology grounded in data production studies, with a focus on training students to document and analyze the socio-technical systems that shape chart outcomes. Students will be introduced to structured data collection practices using spreadsheetbased workflows, where they will record chart rules, release timelines, and the circulation of multiple song versions. Through guided instruction, they will learn how to organize data for filtering, cross-tabulation, and comparative analysis, enabling them to identify relationships between release strategies, platform metrics, and chart performance. 

All data tracing work will be documented in shared spreadsheets, allowing students to track how changes in chart methodology, industry practices, and technological systems interact over time. As a final stage, students will contribute to the development of visual outputs that translate their f indings into accessible formats. These may include a collective infographic mapping key developments in remix and versioning practices, as well as interactive Timeline and StoryMap projects using open-source tools developed by Knight Lab. These outputs introduce students to multiple forms of research communication while making visible the evolving structures of contemporary music charts. Final projects will be shared via www.SongData.ca. 

Phase 1: Training and Orientation (5 hours) – introduction to project goals, key concepts, and data tracing methodology; training in spreadsheet design and data collection  

Phase 2: Background Research (20 hours) – collaborative review of remix culture and chart methodology; development of a timeline of key industry changes 

Phase 3: Case Selection and Research Design (5 hours) – selection of individual songs with multiple versions; development of data collection plan  

Phase 4: Data Collection and Tracing (20 hours) – documenting release timelines and version histories; collecting and organizing chart performance data; and tracking relationships between releases and chart movement  

Phase 5: Analysis and Synthesis (20 hours) – identifying patterns across case studies; interpreting relationships between versioning practices and chart success  

Phase 6: Final Outputs (15 hours) – preparation of a final project (an interactive digital Timeline or StoryMap to share the findings); submission of cleaned datasets. 

= ~85 hrs independent research + ~15 hrs weekly team meetings  

• Data collection, organization, and management skills
• Experience with spreadsheet-based analysis and visualization
• Understanding of music industry metrics and platform economies
• Ability to connect cultural practices to technological and institutional systems
• Research, critical thinking, and writing skills  

Preferred Semester: Fall 2026