Bibliographic record
Abstract
Purpose The purpose of this paper is to report on a comparative and analytical examination of ten social tagging systems' interfaces and their features and functionalities. The specific objective of the study was to examine the ways in which the user interfaces of social tagging systems encourage and provide users with features to assign, explore, browse and make use of tags during their interaction with social tagging sites. Design/methodology/approach The user interface features and functionalities of ten social tagging sites (six social bookmarking and four social media sharing sites) are examined. A categorisation of tag‐related features is developed for analysis. The sites are selected based on such criteria as popularity, variety of site type, and inclusion of tagging features and content type. Findings The findings of this study show that there is an emerging interface design paradigm with respect to social tagging sites that reflects a particular focus on exploratory search and browsing features and services. Some of the key areas discussed are: user tagging features; exploratory and tag browsing features; and interface layout. Practical implications The findings of this study of the user interface features of social tagging sites provide a comprehensive picture of the possible and potential features that can be incorporated into new social tagging systems. Based on the evidence found in the examined social tagging interfaces, recommendations are made on the design of tag posting, tag use, tag browsing, tag lists and tag clouds. The design recommendations offer ideas for the development of more sophisticated exploratory and interactive user interfaces for social tagging systems. Originality/value This is the first paper that reports on a comparative and exploratory examination of social tagging user interface features and functionalities.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".