Decoding the Hit: A Deep Dive into Spotify’s Top 10 Streamed Songs of 2023
- Introduction:
Spotify, a digital giant, shapes our music consumption. Its global network connects artists, songs, and listeners, impacting trends and interactions. Analyzing Spotify’s structure reveals insights into music preferences, streaming, and artist engagement. This post delves into the analysis of Spotify’s network, highlighting 2023’s 10 top streamed songs. By uncovering Spotify’s trends, we showcase how network analysis informs music industry decisions.
- Data-Driven Inquiry: Insights, Stakeholders, and Questions
The primary inquiry revolves around uncovering the 10 most streamed or listened-to track on Spotify throughout 2023. This vital revelation holds significance for a multitude of stakeholders, including music streaming platforms, artists, and music industry analysts. Understanding the prevailing trends and listener preferences is pivotal for these entities. By pinpointing the top-ranking songs, stakeholders can adeptly refine their marketing strategies, optimize playlist curation, and tailor content creation endeavors to align seamlessly with audience expectations. Consequently, this insight plays a pivotal role in shaping decisions related to playlist placements, promotional initiatives, and prospective collaborations between artists and streaming platforms.
- Data Source and Collection
The network data used for this analysis comes from the Top Spotify Songs dataset for the year 2023, obtained from Kaggle. The dataset provides information on various attributes of popular songs, including track name, artist(s) name, release date, streaming statistics, and audio features. To collect this data, I downloaded the dataset from the Kaggle website and imported it into a pandas DataFrame using Python. The dataset contains fields such as track_name, artist(s)_name, streams, bpm, danceability_%, and more, which are relevant for analyzing the most streamed or listened song on Spotify in 2023.
- Cleaning data and addressing bugs
Cleaning the data involved several steps to ensure accuracy and consistency. Common bugs encountered included missing values, inconsistent formatting, and outliers. For missing values, I used methods like imputation or removal, depending on the data’s importance. Inconsistent formatting, such as different date formats, was standardized for uniformity. Outliers in streaming statistics were addressed by examining distribution and removing extreme values that skewed the analysis. Additionally, I checked for duplicate entries and corrected any discrepancies. I also filtered the data to only include entries from the year 2023. These cleaning steps ensured that the data was reliable for analysis, minimizing errors and enhancing insights into Spotify’s top songs of 2023.
- Defining Nodes and Edges:
In our analysis of Spotify’s top 10 streamed songs of 2023, nodes represent individual songs, while edges signify connections between them. We collected song attributes such as track name, artist(s) name, and streaming statistics from the dataset. Using this information, we created nodes for each song with their respective attributes. Edges were established based on similarities between songs, such as shared artists or similar release dates. Python’s pandas library facilitated dataset management, while NetworkX aided in network analysis. This approach allows us to uncover trends and patterns in Spotify’s music landscape by examining relationships between songs. - Importance in the Graph:
In our analysis, importance is determined by the total number of streams accumulated by each artist. Artists with a higher number of streams across multiple songs are considered more important in our graph. - Important Nodes:
1. Morgan Wallen : Known for songs like “Last Night,” “Everything I Love,” and “Born With A Beer In My Hand.”
2. NewJeans : Notable for tracks such as “Super Shy,” “OMG,” and “New Jeans.”
3. Jimin: Recognized for songs like “Like Crazy,” “Set Me Free Pt.2,” and “Like Crazy (English Version).”
These artists have multiple songs in the top 10 streamed list, indicating their popularity and influence among Spotify users. By focusing on these significant nodes, we gain insights into the most streamed artists of 2023 and their impact on the music industry.
- Summarizing findings
Based on the analysis of the top Spotify songs of 2023, we found that certain artists had significantly higher total streams compared to others. By aggregating streams by artist and selecting the top ten artists based on total streams, we focused our analysis on these top artists and their songs. Taylor Swift emerged as the top artist with a total of 6 songs in the top list, indicating a significant presence and popularity of her music among Spotify listeners in 2023.
Using a directed graph representation, we visualized the relationships between the songs of these top artists, based on shared artists. Each node in the graph represents a song, with edges indicating shared artists between songs. The color mapping of nodes and edges helps distinguish between different artists.
· Conclusions and limitations
In concluding our analysis, it’s crucial to acknowledge the limitations of our approach. One major limitation is the reliance on data from a single source, namely Spotify. This may introduce bias towards songs that are more popular on Spotify compared to other platforms. Additionally, the dataset only covers songs from the year 2023, limiting the scope of our analysis to that specific timeframe. Moreover, the dataset may not capture the entirety of listener preferences, as it primarily focuses on streaming statistics and audio features without considering factors like cultural context or regional preferences. These limitations highlight the need for a more comprehensive approach to understanding music trends and listener behavior across various platforms and demographics.
- Data Sources:
Elgiriyewithana, Nidula. “Most Streamed Spotify Songs 2023.” Kaggle, 26 Aug. 2023, www.kaggle.com/datasets/nelgiriyewithana/top-spotify-songs-2023. - Github: https://github.com/larissakimberly4/INST-414-spr22.git
