Individual and social behavior in tagging systems
Bibliographic record
Abstract
In tagging systems users can annotate items of interest with free-form terms. A good understanding of usage characteristics of such systems is necessary to improve the design of current and next generation of tagging systems. To this end, this work explores three aspects of user behavior in CiteULike and Connotea, two systems that include tagging features to support online personalized management of scientific publications. First, this study characterizes the degree to which users re-tag previously published items and reuse tags: 10 to 20% of the daily activity can be characterized as re-tagging and about 75% of the activity as tag reuse. Second, we use the pairwise similarity between users' activity to characterize the interest sharing in the system. We present the interest sharing distribution across the system, show that this metric encodes information about existing usage patterns, and attempt to correlate interest sharing levels to indicators of collaboration such as co-membership in discussion groups and semantic similarity of tag vocabularies. Finally, we show that interest sharing leads to an implicit structure that exhibit a natural segmentation. Throughout the paper we discuss the potential impact of our findings on the design of mechanisms that support tagging systems.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".