Characterizing a social bookmarking and tagging network
Bibliographic record
Abstract
Social networks and collaborative tagging systems are rapidly\ngaining popularity as a primary means for storing and sharing data among \nfriends, family, colleagues, or perfect strangers as long as they have common \ninterests. del.icio.us is a social network where people store and share their \npersonal bookmarks. Most importantly, users tag their bookmarks for ease of \ninformation dissemination and later look up. However, it is the friendship \nlinks, that make delicious a social network. They exist independently of the \nset of bookmarks that belong to the users and have no relation to the tags \ntypically assigned to the bookmarks. To study the interaction among users, the \nstrength of the existing links and their hidden meaning, we introduce\nimplicit links in the network. These links connect only highly “similar” users. \nHere, similarity can reflect different aspects of the user’s profile that makes \nher similar to any other user, such as number of shared bookmarks, or \nsimilarity of their tags clouds. We investigate the question whether friends \nhave common interests, we gain additional insights on the strategies that users \nuse to assign tags to their bookmarks, and we demonstrate that the graphs \nformed by implicit links have unique properties differing from binomial random \ngraphs or random graphs with an expected power-law degree distribution.
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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 | 0.007 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".