Topic reading dynamics of the Chinese Sina-Microblog
Bibliographic record
Abstract
A topic in the Sina-Microblog consists of multiple Weibos sharing a specific set of key words. Therefore, a reader of a Weibo may be susceptible to other Weibos of the same topic, and hence may undergo multiple transitions between the susceptible and the exposed states before eventually becoming infectious. This complicates the reading dynamics that traditional epidemic susceptible-exposed-infectious compartmental models cannot capture, and requires a different setting than a single Weibo forwarding dynamics model. Here we formulate a topic reading dynamics model; introduce some summative indices characterizing the reading “outbreak” potential; derive analytic formulae to calculate these indices; and examine the sensitivity of these indices and the ability of prediction on model parameters and on sampling frequencies. We conduct some numerical experiments based on historical data of popular reading topics in the Chinese Sina-Microblog. In our experiments, different sampling frequencies (4 h, 8 h or 12 h) nearcasting the turning points are feasible, in particular, a good nearcasting prediction for the accumulated R-users are 72 h with the sampling frequencies of every 4 h and every 12 h and 78 h with the sampling frequency of every 8 h.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".