Modeling and analyzing an opinion network dynamics considering the environmental factor
Bibliographic record
Abstract
With the development of Internet technology, social media has gradually become an important platform where users can express opinions about hot events. Research on the mechanism of public opinion evolution is beneficial to guide the trend of opinions, making users' opinions change in a positive direction or reach a consensus among controversial crowds. To design effective strategies for public opinion management, we propose a dynamic opinion network susceptible-forwarding-immune model considering environmental factors (NET-OE-SFI), which divides the forwarding nodes into two types: support and opposition based on the real data of users. The NET-OE-SFI model introduces environmental factors from infectious diseases into the study of network information transmission, which aims to explore the evolution law of users' opinions affected by the environment. We attempt to combine the complex media environmental factors in social networks with users' opinion information to study the influence of environmental factors on the evolution of public opinion. Data fitting of real information transmission data fully demonstrates the validity of this model. We have also made a variety of sensitivity analysis experiments to study the influence of model parameters, contributing to the design of reasonable and effective strategies for public opinion guidance.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".