Skip to content
DraconiticResearch
Search Sources API
186,086 studies31 sourcesUpdated 55 min ago
PreprintarXiv licencearXiv

Charting the life of Billboard hits through memory, turnover, and predictability

Abstract

Rankings shape the visibility and success of cultural products, yet their temporal dynamics remain underexplored when comparing distinct ranked objects within the same domain. Here, we use nearly seven decades of Billboard Hot 100 songs and six decades of Billboard 200 albums to investigate how success emerges, persists, and differs between songs and albums. We find that albums exhibit a heavier-tailed permanence distribution and reenter the charts more often than songs, whereas songs typically have longer uninterrupted runs. Similarity between successive charts decays much faster for songs than for albums, suggesting that individual hits reflect shorter-lived collective attention, while albums retain longer cultural memory. Rank-turbulence divergence shows that consecutive charts are similar, but that top positions are dominated more by rank reshuffling than by turnover. Entropy-based analyses reveal high uncertainty in rank movements, with distinct historical patterns for songs and albums and a strong dependence on trajectory length. Clustering of trajectories shows that chart success is organized into a small number of typical pathways, including canonical rise-and-fall trajectories, high-end persistence, and monotonic decline. Together, these results show that musical charts are not merely records of popularity, but dynamic memory systems in which attention, turnover, and predictability interact differently for songs and albums.

Versions

Related

  1. Noise-resilient collective order through temporal memory in self-propelled particles
    Mihir Durve, Sauro Succi, Antonio Celani
    arXivPreprintCC BY 4.0Full textComputational physicsCondensed matterarXiv
  2. Electrical Control over Volatile Mott Switching through Non-Volatile Memory Effects
    Amihai Kronman, Gil Levi, Yoav Kalcheim
    arXivPreprintCC BY-SA 4.0Full textCondensed matterarXiv
  3. From quantum reservoirs to quantum extreme learning machines through a nearest-neighbor spin chain with tunable quantum memory
    Carlos Ramon-Escandell, Arnau Riera, Marcin Płodzień
    arXivPreprintCC BY 4.0Full textQuantum physics & quantum informationarXiv
  4. Charting the thermodynamic stability of hybrid perovskite alloys with machine learning
    Jarno Laakso, Armi Tiihonen, Patrick Rinke
    arXivPreprintCC BY 4.0Full textCondensed matterarXiv
  5. Local Predictability and Collective Fidelity in LLM-Agent Societies
    Igor Itkin
    arXivPreprintCC BY 4.0Full textStatistical & nonlinear physicsarXiv
BibTeX
@misc{pessa2026charting,
  title = {{Charting the life of Billboard hits through memory, turnover, and predictability}},
  author = {Arthur A. B. Pessa and Angelo A. Flores and Matjaz Perc and Haroldo V. Ribeiro},
  howpublished = {arXiv},
  year = {2026},
  month = jul,
  doi = {10.48550/arxiv.2607.11446},
  eprint = {2607.11446},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.11446},
}
RIS
TY  - UNPB
TI  - Charting the life of Billboard hits through memory, turnover, and predictability
AU  - Arthur A. B. Pessa
AU  - Angelo A. Flores
AU  - Matjaz Perc
AU  - Haroldo V. Ribeiro
PY  - 2026
DA  - 2026/07/13
PB  - arXiv
DO  - 10.48550/arxiv.2607.11446
UR  - https://arxiv.org/abs/2607.11446
AB  - Rankings shape the visibility and success of cultural products, yet their temporal dynamics remain underexplored when comparing distinct ranked objects within the same domain. Here, we use nearly seven decades of Billboard Hot 100 songs and six decades of Billboard 200 albums to investigate how success emerges, persists, and differs between songs and albums. We find that albums exhibit a heavier-tailed permanence distribution and reenter the charts more often than songs, whereas songs typically have longer uninterrupted runs. Similarity between successive charts decays much faster for songs than for albums, suggesting that individual hits reflect shorter-lived collective attention, while albums retain longer cultural memory. Rank-turbulence divergence shows that consecutive charts are similar, but that top positions are dominated more by rank reshuffling than by turnover. Entropy-based analyses reveal high uncertainty in rank movements, with distinct historical patterns for songs and albums and a strong dependence on trajectory length. Clustering of trajectories shows that chart success is organized into a small number of typical pathways, including canonical rise-and-fall trajectories, high-end persistence, and monotonic decline. Together, these results show that musical charts are not merely records of popularity, but dynamic memory systems in which attention, turnover, and predictability interact differently for songs and albums.
ER  -