Background: Many countries show low COVID-19 vaccination rates despite high levels of readiness and delivery of vaccines. The public's misperceptions, hesitancy, and negative emotions toward vaccines are psychological factors discouraging vaccination. At the individual level, studies have revealed negative perceptual/behavioral outcomes of COVID-19 information exposure via social media where misinformation and vaccine fear flood. Objective: This study extends research context to the global level and investigates social media discourse on the COVID-19 vaccine and its association with vaccination rates of 192 countries in the world. Methods: COVID-19 vaccine tweets were compared by country in terms of (1) the number per million Twitter users, (2) mentions of adverse events-death, side-effects, blood clots, (3) negative sentiment (vs. positive), and (4) fear, sadness, or anger emotions (vs. joy). Artificial intelligence (AI) was adopted to classify sentiment and emotions. Such tweets and covariates (COVID-19 morbidity and mortality rates, GDP, population size and density, literacy rate, democracy index, institutional quality, human development index) were tested as predictors of vaccination rates in countries. Results: Over 21.3 million COVID-19 vaccine tweets posted between November 2020 and August 2021 worldwide were included in our analysis. The global average of COVID-19 vaccine tweets mentioning adverse events was 2% for 'death', 1.15% for 'side-effects', and 0.80% for 'blood clots'. Negative sentiment appeared 1.90 times more frequently than positive sentiment. Fear, anger, or sadness appeared 0.70 times less frequently than joy. The mention of 'side-effects' and fear/sadness/anger emotions appeared as significant predictors of vaccination rates, along with the human development index. Conclusions: Our findings indicate that global efforts to combat misinformation, address negative emotions, and promote positive languages surrounding COVID-19 vaccination on social media may help increase global vaccination uptakes.
Keywords: COVID-19; Twitter; adverse events; artificial intelligence (AI); country; emotions; global; infodemic; multinational; sentiment; side-effect; social media; vaccine.