Social Media Metrics and Popular Legitimacy: Content Analysis of Pre– and Post–COVID-19 Public Engagement With the World Health Organization on X
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) plays a critical role in global health governance, but its popular legitimacy, a measure of public trust and support, has been contested, particularly during crises such as the COVID-19 pandemic. While legitimacy is widely studied through normative and elite-focused approaches, empirical assessments using public discourse remain limited. Social media platforms like X (formerly Twitter) offer real-time data for evaluating public sentiment toward the WHO. OBJECTIVE: This study aims to assess the evolution of the WHO's popular legitimacy from 2008 to 2021 by analyzing public engagement metrics on X, with a particular focus on changes during the COVID-19 pandemic. METHODS: We analyzed 46,667 tweets from the WHO using computational methods, including the retweet-to-reply ratio, sentiment analysis, and longitudinal trend evaluation. Metrics such as likes, retweets, and replies were examined to quantify public sentiment, with the retweet-to-reply ratio serving as a key indicator of controversy and support levels. RESULTS: The WHO's popular legitimacy was stable from 2008 to 2019 but declined significantly during the COVID-19 pandemic, reflecting heightened public scrutiny and criticism. Engagement metrics revealed increased replies relative to retweets during this period, indicating greater controversy in public discourse. CONCLUSIONS: This study demonstrates the feasibility of using social media metrics to measure international organization (IO) legitimacy over time. The findings highlight the impact of global crises on public trust and provide a replicable framework for assessing the legitimacy of other IOs. Social media engagement offers valuable insights for IOs to adapt communication strategies and maintain public trust during crises.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".