The basics of health educational activities concerning vaccination in the Internet: “rational” advocates and “emotional” opponents
Bibliographic record
Abstract
The Internet and social media are becoming an influential source of information regarding health care issues, including vaccination. The profound analysis of the Russian Internet discourse on vaccination demonstrated that nowadays there is no clear-cut understanding of adequate strategy concerning informational policy in this direction. The article defines the principles of information policy for pro-vaccine attitudes spreading on the Internet. METHODS: The sampling consisted of the Muscovites Internet messages containing the keywords 'vaccine' and 'vaccinations' and their derivatives. The analyzed period of data export is the first quarter of 2019. The size of uploading was 19948 messages, the random sample of 800 messages and 280 images was taken. Veterinary and spam messages were excluded. RESULTS: The topic of vaccines in our sample is discussed more frequently by women (72%). The average age of participants is 35. The algorithms of vaccination, complications, and necessity of vaccines are the most frequently discussed issues. In our sample pro and contra vaccines messages are of equal percentage (42% and 41% respectively) and there are 17% of those who are in doubt. The key argument for contra vaccines is post-vaccination complications. The visual propaganda of vaccine supporters is aimed at the ideas of common good, rationality, scientific knowledge, they are using lots of humor, and it usually requires the context awareness. The opponents apply more personal and emotional approach. their values are family and personal experience, common sense. Their approach is more appealing to the 'common' reader. CONCLUSION: The article describes strategies for argumentation and pro and contra vaccine propaganda on the Internet. The research offers its results to the segment users concerning their rational and emotional reactions. The work strategy with each group is proposed.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".