Reuse, temporal dynamics, interest sharing, and collaboration in social tagging systems
Bibliographic record
Abstract
User–generated content shapes the dynamics of the World Wide Web. In particular, collaborative tagging represents a simple, yet powerful, feature that enables users to share and collaboratively annotate content such as photos and URLs. This collaborative behavior and the pool of user–generated metadata create opportunities to improve existing systems and to design new mechanisms. However, to realize this potential, it is necessary to first understand the usage characteristics of current systems.This work addresses this issue by characterizing three tagging systems (CiteULike, Connotea and del.icio.us) while focusing on three aspects: i) the patterns of information (tags and items) production; ii) the temporal dynamics of users’ tag vocabularies; and, iii) the social aspects of tagging systems. The analysis of the patterns of information production shows that users publish new content more often than they annotate already existing content in the system. The opposite, however, occurs for tags; the level of tag reuse is much higher. The study of the temporal dynamics of user vocabularies shows that the growth rate of tag vocabularies across the user population over time decreases at early ages, stabilizes, and returns to increase for older users. Moreover, a closer look into the change of vocabulary contents over time shows that despite the fact that tag vocabularies are slowly growing in size with user age, the relative frequency in which each tag is used converges relatively quickly in a users lifetime. Finally, the characterization of social aspects of tagging unveils the relationship between the implicit user ties, as inferred from the similarity between users’ activity, and their explicit social ties, as represented by co–membership in discussion groups or semantic similarity between tag vocabularies.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| 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".