Most Influential Producers Within the Network of the ‘500 Greatest Songs of All Time’
Most of us have probably heard of many songs in this what the Rolling Stone magazine considers the ‘500 Greatest Songs of All Time’. These songs are what shaped popular culture and have been influential throughout a lot of lives. Making it to this top 500 list could not have been easy given how long the music industry has existed and the sheer amount of music that comes out. However, beyond the the artist are the producers and collaborations that go behind the scenes to create the music. This made me wonder, are the same producers behind some of the biggest hits to ever exist?
The most pertinent question in my analysis is “Which producers wield the most connections to the Top 500 Songs?”. This question is to the interest of music industry professionals and artists, who may want to identify proven producers who have a lot of connections to the top 500 hits. This answer to this question will inform these stakeholders on who they should contact for collaborations or partnerships, given their record of successfully making it to the top 500 list.
To execute this analysis, I levereged the ‘500 Greatest Songs of All Time’ dataset scraped from the publicly available website Rolling Stone. This dataset includes information on song title, song description, where did it appear, artist/s who sang the song, the song writer/s, the song producer, release date, how long it was in the top list, and song position during it’s streak. By examining the connections between the music and producers, I can figure out the underlying network structure and identify the most influential producer nodes in this top 500 list.
I procured this data from Kaggle. The data was collected using web scraping techniques to extract information from the Rolling Stone website. The dataset provides the Rolling Stone’s comprehensive coverage of the top songs spanning eras in popular culture, making it a good resource for our analysis.
Cleaning this data involved handling various aspects. First, I handled the producer names since they were initially stopred as a single string separated by commas. To extract individual producer names, I split those strings. Further more, I made sure to include only the columns I cared about, which was title, artist, and producer. Titles were the songs that producers have worked on, while the producers were what I created nodes and edges based off of.
In my network, the nodes represent the producers, while edges signify collaborative relationships between these producers. These collaborations in productions happen often in music, and it just means multiple names can be put down under the ‘producers’ column in this dataset. For example, an edge between a producers indicates that the producer contributed to the creation of a song in the Top 500.
In this analysis, ‘importance’ can be defined based on how many collaborations these producers have in the top 500. Producers with a larger number of collaborations may be considered more influential as it is proof they have worked on multiple tracks in the top 500 list. The producers with more collaborations have bigger labels and nodes. The top 3 most influential producers are Young with 6 collaborations, Stills with 5 collaborations, and Karl Richardson with 5 collaborations. Below is an example of the code output showing the top 3 producers along with the number of collaborations (or edges) they have, along with their degree centrality values, which represents their importrance in the network based on the number of collaborations they are involved in.
Upon analyzing the Top 500 Songs dataset, I have discovered several influential producers who have left a significant mark. A lot of the top 500 songs of all time are have artist names you may recognize such as the Beatles or Bob Dylan, but now you can view the names of the producers behind a lot of those same hits. By identifying these influential figures, music industry professionals can drive collaborations with these people knowing they are effective producers. For example, these stakeholders may want to only work with top 500 producers that have not just made a one hit wonder, but multiple. They could also specifically want to connect with producers that are known to collaborate with producers that also created top 500 music, to potentially expand their connections even further with known producing experts.
There are many limits to this simple analysis. First of all, the dataset itself is limited to top 500 songs based on Rolling Stone magazine. This means it may not represent the entire music industry comprehensively, and is more likely to simply represent popular culture. This dataset’s focus on popular songs could introduce bias towards already well-known artists, potentially overlooking contributions from more niche music. The analysis also only represents producers as nodes and collaborations as edges, in order to be simple. What this overlooks is the contributions of other influential figures and factors such as writers, record labels, or other industry standard figures that contribute to the success of a song. This analysis also defines ‘importance’ based on number of collaborations, however, importance in the music industry is more subjected and multifaceted. It could include factors such as artistic influence, cultural impact, or commercial success. Focusing solely on collaboration frequency may oversimplify the concept of importance and overlooks those other factors I listed.
In conclusion, this network analysis offers a simple but powerful framework for understanding the connections between producers in the Top 500 Songs dataset by their collaborations. By indentifying influential producers (nodes) and uncovering the underlying structure, stakeholders in the music industry can quickly glance at the bigger values and can make informed decisions to their own benefits, including driving collaborations and fostering connections. Eventually, these same producers can continue to shape popular music for the forseeable future, and maybe they can maintain their spots in the top 500 greatest of all time.
Github repository link for the code I used: INST414/module2.py at main · vitamyon/INST414 · GitHub
