ABSTRACT
BackgroundCOVID-19 vaccination has faced a range of challenges from supply-side barriers such as insufficient vaccine supply and negative information environment and demand-side barriers centring on public acceptance and confidence in vaccines. This study assessed global spatiotemporal trends in demand- and supply-side barriers to vaccine uptake using COVID-19-related social media data and explored the country-level determinants of vaccine acceptance. MethodsWe accessed a total of 13,093,406 tweets sent between November 2020 and March 2022 about the COVID-19 vaccine in 90 languages from 135 countries using Meltwater(R) (a social listening platform). Based on 8,125 manually-annotated tweets, we fine-tuned multilingual deep learning models to automatically annotate all 13,093,406 tweets. We present spatial and temporal trends in four key spheres: (1) COVID-19 vaccine acceptance; (2) confidence in COVID-19 vaccines; (3) the online information environment regarding the COVID-19 vaccine; and (4) perceived supply-side barriers to COVID-19 vaccination. Using univariate and multilevel regressions, we evaluated the association between COVID-19 vaccine acceptance on Twitter(R) and (1) country-level characteristics regarding governance, pandemic preparedness, trust, culture, social development, and population demographics; (2) country-level COVID-19 vaccine coverage; and (3) Google(R) search trends on adverse vaccine events. FindingsCOVID-19 vaccine acceptance was high among Twitter(R) users in Southeast Asian, Eastern Mediterranean, and Western Pacific countries, including India, Indonesia, and Pakistan. In contrast, acceptance was relatively low in high-income nations like South Korea, Japan, and the Netherlands. Spatial variations were correlated with country-level governance, pandemic preparedness, public trust, culture, social development, and demographic determinants. At the country level, vaccine acceptance sentiments expressed on Twitter(R) predicted higher vaccine coverage. We noted the declining trend of COVID-19 vaccine acceptance among global Twitter(R) users since March 2021, which was associated with increased searches for adverse vaccine events. Interpretation In future pandemics, new vaccines may face the potential low-level and declining trend in acceptance, like COVID-19 vaccines, and early responses are needed. Social media mining represents a promising surveillance approach to monitor vaccine acceptance and can be validated against real-world vaccine uptake data. FundingNational Natural Science Foundation of China.
ABSTRACT
ObjectiveWe sought to understand how U.S. residents responded to COVID-19 as it emerged, and the extent to which spatial-temporal factors impacted response. Materials and MethodsWe mined and reverse-geocoded 269,556 coronavirus-related social media postings on Twitter from January 23rd to March 25th, 2020. We then ranked tweets based on the socioeconomic status of the county they originated from using the Area Deprivation Index (ADI); that we also used to identify areas with high initial disease counts ("hotspots"). We applied topic modeling on the tweets to identify chief concerns and determine their evolution over time. We also investigated how topic proportions varied based on ADI and between hotspots and non-hotspots. ResultsWe identified 45 topics, which shifted from early-outbreak-related content in January, to the presidential election and governmental response in February, to lifestyle changes in March. Highly resourced areas (low ADI) were concerned with stocks, social distancing, and national-level policies, while high ADI areas shared content with negative expression, prayers, and discussion of the CARES Act economic relief package. Within hotspots, these differences stand, with the addition of increased discussion regarding employment in high ADI versus low ADI hotspots. DiscussionTopic modeling captures the major concerns in COVID-19-related discussion on a social media platform in the early months of the pandemic. Our study extends previous studies that utilized topic modeling on COVID-19 related tweets and linked the identified topics to socioeconomic status using ADI. Comparisons between low and high ADI areas indicate differential Twitter discussions, corresponding to greater concern with economic hardship and impacts of the pandemic in less resourced communities, and less focus on general public health messaging. ConclusionThis work demonstrates a novel framework for assessing differential topics of conversation correlating to income, education, and housing disparities. This, with integration of COVID-19 hotspots, offers improved analysis of crisis response on Twitter. Such insight is critical for informed public health messaging campaigns in future waves of the pandemic, which should focus in part specifically on the interests of those who are most vulnerable in the lowest resourced health settings.
