Social Interaction History: A Framework for Supporting Exploration of Social Information Spaces
Bibliographic record
Abstract
Social interaction history refers to traces of social interaction in information spaces. These traces have potential to help users explore and navigate through information spaces. In digital spaces, however, past records of social interaction are often hidden or underutilized. As a result, users cannot use social navigation to guide their movement while browsing information collections. We developed a conceptual framework for using social interaction history to improve information exploration. The basic hypothesis of our framework is that social interaction history can serve as a good indicator of the potential value of information items. We tested our hypothesis by performing statistical analysis of a data set from a social Web application. The results supported our hypothesis: there were significant positive relationships between traces of social interaction and the degree of interestingness of Web articles. We discuss the implications of these findings for the design of social applications and the potential applications of social interaction history in various domains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".