Vaccination Conversations on X in Spanish and Catalan: Qualitative Content Analysis
Bibliographic record
Abstract
BACKGROUND: The analysis of social networks should be considered by institutions and governments alongside surveys and other conventional methods for assessing public attitudes toward vaccines. X (formerly known as Twitter) has emerged as a significant source for studying vaccine hesitancy. OBJECTIVE: The aim of the study is to examine the main arguments and narratives in favor and against vaccination expressed in Spanish- and Catalan-language posts, comments, and opinions on the social media platform X. METHODS: Spanish and Catalan posts were collected from X using NodeXL Pro between March and December 2021, resulting in 479,734 posts. For qualitative analysis, a random subsample of 384 tweets was selected using Cochran's formula (95% confidence and ±5% margin of error). A bespoke code frame was developed in collaboration with medical and social media experts, and posts were translated into English. Intercoder reliability, assessed on 20% of the sample, yielded 93.4% agreement and a Cohen κ of 0.92. RESULTS: A total of 479,734 posts were retrieved from 29,706 users. After an inductive review of the data, six themes were identified, which formed the basis of our code frame: (theme 1) vaccine acquisition and distribution, (theme 2) vaccine skepticism and criticism, (theme 3) provaccination stance, (theme 4) global COVID-19 situation, (theme 5) vaccine politics and international relations, and (theme 6) miscellaneous news and posts. Vaccine skepticism and criticism was the most frequent theme (93/384, 24.2%), whereas vaccine politics and international relations was the least (25/384, 6.5%). We observed that while some posts supported vaccination, others expressed concerns about vaccine safety and efficacy, promoted conspiracy theories, disseminated misinformation, or opposed scientific consensus. Challenges related to vaccine acquisition and distribution within specific countries were also identified, along with political and economic factors, such as the politicization of vaccines, which hindered equitable distribution between vaccine-producing and vaccine-needing countries. Additionally, the pandemic's social impact fostered community support initiatives and solidarity. CONCLUSIONS: Our findings can inform measures to promote vaccine acceptance and reinforce trust in health care systems, professionals, and scientific perspectives, thereby improving vaccination coverage. These insights may serve as a foundation for developing sociopolitical strategies to enhance vaccination management and address future pandemics or new vaccination campaigns.
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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 | 0.001 |
| 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".