Modelling Member Behaviour in on-Line User-Generated Content Sites: A Semiparametric Bayesian Approach
Bibliographic record
Abstract
Summary We develop a model to understand and describe the inherent behaviours and interactions of members over time through the medium of user-generated content (UGC) in an on-line community. Because the behavioural event counts of generating and accessing UGC by on-line users serve as the two most important metrics to judge the success of on-line communities, we propose a bivariate zero-inflated Poisson model to model simultaneously the daily counts of the two main UGC-related activities. In particular, we consider interdependences of the repeatedly measured behavioural events within members, model the dependence of the current event counts on the past event counts and explore the probable non-linear effects of time at the individual level. Furthermore, we incorporate interactions between members by constructing a set of individual-specific time varying measures in an integrated modelling framework. In our empirical applications, we examine key behavioural determinants influencing member behaviours in the UGC site. As part of our substantive contribution, we highlight the model’s ability to make accurate predictions about the evolution of the on-line community.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".