Detection of Twitter Users' Attitudes about Flu Vaccine based on the Content and Sentiment Analysis of the Sent Tweets
Bibliographic record
Abstract
Introduction: The influenza vaccine is one of the controversial challenges in today's societies. Considering the importance of using the flu vaccine in preventing the spread of influenza virus, the Twitter network, as a rich source of data, provides suitable conditions for research in this field to examine the attitudes of different people about this vaccine. The results in one hand will help health authorities to make more comprehensive decisions on long-term health plans for people with an awareness of the attitude of individuals towards the flu vaccine; on the other hand, it is concerned with data miners. Method: In this review study, approximately 1.220.539 tweets have been gathered from the Twitter social network during a one month period and have been clustered by using Mallet software. Categorizing users and separating tweets have done with an appropriate approximation. The content and sentiments of the selected tweets were analyzed and the locations of the users were checked. Results: In sentiments analysis, 76.28% of the tweets had a positive weight, 1.87% was neutral, and 19.68% had negative weight, indicating a positive attitude about the influenza vaccine. The location of users showed the highest rate of sending tweets from Canada, Britain and the United States, and Asian countries, and in particular, Iran, had a very small percentage of it. Conclusion: The highest rate of positive tweets were respectively sent by ‘individual’, ‘health’, and ‘organization’ groups indicating the global acceptance of the influenza vaccine and the success of the global health organizations on vaccination.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".