Understanding the Characteristics of Internet Short Video Sharing: YouTube as a Case Study
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
Established in 2005, YouTube has become the most successful Internet site providing a new generation of short video sharing service. Today, YouTube alone comprises approximately 20% of all HTTP traffic, or nearly 10% of all traffic on the Internet. Understanding the features of YouTube and similar video sharing sites is thus crucial to their sustainable development and to network traffic engineering. In this paper, using traces crawled in a 3-month period, we present an in-depth and systematic measurement study on the characteristics of YouTube videos. We find that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length and access pattern, to their active life span, ratings, and comments. The series of datasets also allows us to identify the growth trend of this fast evolving Internet site in various aspects, which has seldom been explored before. We also look closely at the social networking aspect of YouTube, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders' choices form a small-world network. This suggests that the videos have strong correlations with each other, and creates opportunities for developing novel caching or peer-to-peer distribution schemes to efficiently deliver videos to end users.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| 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