Bibliographic record
Abstract
The spread of misinformation on social media can trigger panic among those who encounter and believe it.To combat this issue and reduce panic, we have formulated a theoretical framework known as the dynamic rumor propagation model.Our model is agent-based and operates at the individual level.It extends the dynamic 8-state ICSAR (Ignorance, Information Carrier, Information Spreader, Information Advocate, Removal) rumor propagation model, which is typically applied at the population level.Moving to an individual level allows us to explore personal traits and decision-making processes, like how an individual's stubbornness affects the degree of opinion change resulting from social media interactions.While the ICSAR model conducted control variable tests at the population level, we performed similar tests with our agent-based model.Our results align with the results in the ICSAR model, showing that misinformation is mainly spread by individuals who tend to trust easily and have poor judgment skills.Although our model has the advantage of enabling adjustments to individual behaviors during simulations, this remains as future work.This capability marks a shift from simply tweaking parameters at the population level and opens avenues for future research, like programming some agents to spread information against false information.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".