Understanding Video Sharing Propagation in Social Networks
Bibliographic record
Abstract
Modern online social networking has drastically changed the information distribution landscape. Recently, video has become one of the most important types of objects spreading among social networking service users. The sheer and ever-increasing data volume, the broader coverage, and the longer access durations of video objects, however, present significantly more challenges than other types of objects. This article takes an initial step toward understanding the unique characteristics of video sharing propagation in social networks. Based on realworld data traces from a large-scale online social network, we examine the user behavior from diverse aspects and identify different types of users involved in video propagation. We closely investigate the temporal distribution during propagation as well as the typical propagation structures, revealing more details beyond stationary coverage. We further extend the conventional epidemic models to accommodate diverse types of users and their probabilistic viewing and sharing behaviors. The model, effectively capturing the essentials of the propagation process, serves as a valuable basis for such applications as workload synthesis, traffic prediction, and resource provision of video servers.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".