I-SFI model of propagation dynamic based on user’s interest intensity and considering birth and death rate
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
Everyone has a different level of interest in a trending topic posted on social media, which may affect user’s behaviour. In order to find out the way it affects the process of information transmission, we construct an interest intensity-based susceptible-forwarding-immune$\left({I - SFI} \right)$I−SFI propagation dynamic model and two parameters birth rate$ A$A and death rate$ \mu $μ are introduced to represent the users who newly join the group of disseminated information and the users who leave this population. And we give different birth rates to people with various levels of interest, which helps us to determine the interest intensity of potential users to a certain extent. We use the forwarding data of the real topic on Chinese Sina-microblog for data fitting, which can accurately parameterize the model and quantify the impact of interest intensity. And sensitivity analyses also give some strategies for increasing the impact of information dissemination process from the perspective of interest intensity.
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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