RESUMEN
Research has repeatedly demonstrated the ability of social networks, interpersonal discussion, and perceived social norms to shape health-related outcomes. There are still substantial gaps, however, in understanding the theoretical mechanism that holds these components together, as well as the boundary conditions of their effects. Employing ego-network analysis with a representative sample of Illinois residents (N = 711) and focusing on the context of COVID-19 vaccine adherence, this study constructs a comprehensive framework to examine the direct, indirect, and conditional relationships linking social capital within networks, factual knowledge, and vaccination. Overall, the results point to the ability of tight-knit networks to influence knowledge and behavior for better or worse, depending on the composition of the network and its conversational valence. Theoretical and practical implications are discussed.
RESUMEN
Drawing upon theories of risk and decision making, we present a theoretical framework for how the emotional attributes of social media content influence risk behaviors. We apply our framework to understanding how COVID-19 vaccination Twitter posts influence acceptance of the vaccine in Peru, the country with the highest relative number of COVID-19 excess deaths. By employing computational methods, topic modeling, and vector autoregressive time series analysis, we show that the prominence of expressed emotions about COVID-19 vaccination in social media content is associated with the daily percentage of Peruvian social media survey respondents who are vaccine-accepting over 231 days. Our findings show that net (positive) sentiment and trust emotions expressed in tweets about COVID-19 are positively associated with vaccine acceptance among survey respondents one day after the post occurs. This study demonstrates that the emotional attributes of social media content, besides veracity or informational attributes, may influence vaccine acceptance for better or worse based on its valence.