Identifying active subgroups in online communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As online communities proliferate, methods are needed to explore and capture patterns of activity within them. This paper focuses on the problem of identifying active subgroups within online communities. k-plex analysis and hierarchical clustering are used to identify and contrast subgroups, and the methodology is demonstrated in a case study involving the TorCamp Google group community. We assessed the validity of the subgroups obtained in the case study by comparing them with the members' experienced sense of community, and their self-reported acquaintanceships. Results suggest that active subgroups of people not only interact with each other at a higher rate, but also have a greater experienced sense of community. It is concluded that detection of active subgroups in online communities can be implemented widely using automated tools for analyzing the social networks implied by online interactions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it