Modeling and Analyzing Information Propagation Evolution Integrating Internal and External Influences
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
Abstract Online social networks have revolutionized communication, providing individuals with platforms to express their personal opinions on diverse topics. Researchers have independently explored information propagation and opinion evolution within complex networks. However, these phenomena exhibit interconnectedness, where information dissemination influences opinion evolution and vice versa. To address challenges in complex network modeling and opinion‐information coupling, internal and external factors are considered in public opinion scenarios by incorporating the crowd effect, enhancement effect, and evolutionary game theory. The susceptible‐latent‐forwarding‐immune‐Jager‐Amblard (SLFI‐JA) model is presented by modifying the SLFI propagation dynamics model and the JA opinion dynamics model, enabling the integration of information propagation and opinion evolution at the microlevel. Through analyzing real‐world social hotspots on Sina Weibo, case studies and comparative analyses are conducted to validate the rationality and effectiveness of the proposed model. Furthermore, the findings identify key factors influencing public opinion dissemination and group opinion evolution, offering valuable insights to relevant departments in public opinion response and management. The study aims to mitigate the harmful effects of negative public opinions, prevent extreme adverse online events, and foster a healthier online environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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