Propagation-based social-aware multimedia content distribution
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
Online social networks have reshaped how multimedia contents are generated, distributed, and consumed on today's Internet. Given the massive number of user-generated contents shared in online social networks, users are moving to directly access these contents in their preferred social network services. It is intriguing to study the service provision of social contents for global users with satisfactory quality of experience. In this article, we conduct large-scale measurement of a real-world online social network system to study the social content propagation. We have observed important propagation patterns, including social locality, geographical locality, and temporal locality. Motivated by the measurement insights, we propose a propagation-based social-aware delivery framework using a hybrid edge-cloud and peer-assisted architecture. We also design replication strategies for the architecture based on three propagation predictors designed by jointly considering user, content, and context information. In particular, we design a propagation region predictor and a global audience predictor to guide how the edge-cloud servers backup the contents, and a local audience predictor to guide how peers cache the contents for their friends. Our trace-driven experiments further demonstrate the effectiveness and superiority of our design.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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