Popularity or Proximity: Characterizing the Nature of Social Influence in an Online Music Community
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We study social influence in an online music community. In this community, users can listen to and “favorite” (or like) songs and follow the favoriting behavior of their social network friends—and the community as a whole. From an individual user’s perspective, two types of information on peer consumption are salient for each song: total number of favorites by the community as a whole and favoriting by their social network friends. Correspondingly, we study two types of social influence: popularity influence, driven by the total number of favorites from the community as a whole, and proximity influence, due to the favoriting behavior of immediate social network friends. Our quasi-experimental research design applies a variety of empirical methods to highly granular data from an online music community. Our analysis finds robust evidence of both popularity and proximity influence. Furthermore, popularity influence is more important for narrow-appeal music compared to broad-appeal music. Finally, the two types of influence are substitutes for one another, and proximity influence, when available, dominates the effect of popularity influence. We discuss implications for design and marketing strategies for online communities, such as the one studied in this paper.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it