Imagine All the People: Characterizing Social Music Sharing on Reddit
Bibliographic record
Abstract
Music shapes our individual and collective identities, and in turn is shaped by the social and cultural contexts it occurs in. Qualitative approaches to the study of music have uncovered rich connections between music and social context but are limited in scale, while computational approaches process large amounts of musical data but lack information on the social contexts music is embedded in. In this work, we develop a set of neural embedding methods to understand the social contexts of online music sharing, and apply them to a novel dataset containing 1.3M instances of music sharing in Reddit communities. We find that the patterns of how people share music in public are related to, but often differ from, how they listen to music in private. We cluster artists into social genres that are based entirely on aggregate sharing patterns and reflect where artists are invoked. We also characterize the social and cultural contexts music is shared in by measuring associations with social dimensions such as age and political affiliation. Finally, we observe that a significant amount of sharing is attributable to extra-musical factors—additional meanings that people have associated with songs. We develop two methods to quantify the extra-musicality of music sharing. Our methodology is widely applicable to the study of online social contexts, and our results reveal novel cultural associations that contribute to a better understanding of the online music ecosystem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".