The Dissemination of Social Media and Pop Music: Influence and Trends
DOI:
https://doi.org/10.56294/mr2025137Keywords:
Pop Music, Social Media Dissemination, Music Streaming Platforms, Digital Music Trends, Music PopularityAbstract
Social media has emerged as a dominant force in reshaping pop music is circulated, dissemination, and popularized. Traditional models of music dissemination have shifted toward platform-driven dynamics, allowing rapid trend formation and widespread audience engagement. This research aims to analyze the role of social media platforms in the dissemination of pop music and to identify emerging trends and influence patterns that affect music popularity in the digital era. A quantitative approach is employed using data from 476 pop songs that appeared on the TikTok, Instagram, YouTube, Spotify, and Viral charts from 2018 to 2024. Metrics included are engagement rates (likes, shares, comments), streaming volumes, hashtag usage, platform of origin, and viral duration. Statistical techniques such as descriptive analysis, Pearson correlation, multiple linear regression, and time-series trend analysis are applied to examine relationships and predict dissemination behavior. Findings indicate a strong correlation between TikTok engagement and Spotify streaming volumes (β = 0.33, p < 0.001). Regression analysis showed that social media metrics explained 69% of the variance in streaming popularity. Time-series analysis revealed that viral songs peak earlier and fade faster than songs promoted by traditional media. Social media significantly influences the dissemination of pop music, accelerating exposure and shaping listener behavior. While it democratizes access to audience attention, it also introduces volatility and short-lived popularity cycles, suggesting a dual role in amplifying and destabilizing music trends.
References
Zhao Z. Analysis on the “Douyin (Tiktok) Mania” phenomenon based on recommendation algorithms[C]//E3S Web of Conferences. EDP Sciences, 2021, 235: 03029.
Zhang M, Liu Y. A commentary of TikTok recommendation algorithms in MIT Technology Review 2021[J]. Fundamental Research, 2021, 1(6): 846-847.
Deldjoo Y, Schedl M, Hidasi B, et al. Multimedia recommender systems: Algorithms and challenges[M]//Recommender systems handbook. New York, NY: Springer US, 2021: 973-1014.
Yanti D, Subagja A D, Nurhayati S, et al. Short Videos & Social Media Algorithms: Effective Communication in Tourism Marketing[J]. International Journal of Artificial Intelligence Research, 2024, 6(1.2).
Kirdemir B, Kready J, Mead E, et al. Examining video recommendation bias on YouTube[C]//International Workshop on Algorithmic Bias in Search and Recommendation. Cham: Springer International Publishing, 2021: 106-116.
Zhao H, Wagner C. How TikTok leads users to flow experience: investigating the effects of technology affordances with user experience level and video length as moderators[J]. Internet Research, 2022, 33(2): 820-849.
Khoo O. Picturing diversity: Netflix’s inclusion strategy and the Netflix recommender algorithm (NRA)[J]. Television & New Media, 2023, 24(3): 281-297.
Fiallos A, Fiallos C, Figueroa S. Tiktok and education: Discovering knowledge through learning videos[C]//2021 Eighth International Conference on EDemocracy & EGovernment (ICEDEG). IEEE, 2021: 172-176.
Qin Y, Omar B, Musetti A. The addiction behavior of short-form video app TikTok: The information quality and system quality perspective[J]. Frontiers in Psychology, 2022, 13: 932805.
Zhan R, Pei C, Su Q, et al. Deconfounding duration bias in watch-time prediction for video recommendation[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022: 4472-4481.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Mingyuan Chen, Kim Hyun Tai (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.