Analysis of Social Network Structure and Communication Efficacy Based on Algorithmic Optimization in Marxist Mass Communication Strategy
Bibliographic record
Abstract
With the rapid development of social networks, the powerful interactive function of social networks and the high degree of user participation make the information generated in large quantities and spread rapidly, which also provides a dissemination path for the mass dissemination of Marxism. The article analyzes the overall structure of complex social networks and node centrality indexes for the overall topological characteristics of the networks, in order to further analyze the information dissemination characteristics of social networks for the mass dissemination of Marxism. Subsequently, a social network information dissemination model based on SIR is established according to the specific structural characteristics and dissemination modes of the social network, and the experimental test of the information dissemination effect is carried out. Finally, a Marxist mass communication strategy is proposed based on the experimental results. In the experiments on the effect spreading of node information, the nodes numbered 4, 12, and 20 have the strongest information spreading effect, with the number of nodes infected exceeding 30, and the corresponding number of interaction activities are 575, 511, and 663.This suggests that individuals within the aggregated group can build trust with the group members by participating in frequent interactions to improve the effect of information spreading. The development and dissemination of Marxist mass social networks cannot be separated from a series of measures such as a sound social network regulatory system.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".