Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019–June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.

This study examined whether killings of George Floyd, Ahmaud Arbery, and Breonna Taylor by current or former law enforcement officers in 2020 were followed by shifts in public sentiment toward Black people. Methods: Google searches for the names “Ahmaud Arbery,” “Breonna Taylor,” and “George Floyd” were obtained from the Google Health Application Programming Interface (API). Using the Twitter API, we collected a 1% random sample of publicly available U.S. race-related tweets from November 2019–September 2020 (N = 3,380,616). Sentiment analysis was performed using Support Vector Machines, a supervised machine learning model. A qualitative content analysis was conducted on a random sample of 3,000 tweets to understand themes in discussions of race and racism and inform interpretation of the quantitative trends. Results: The highest rate of Google searches for any of the three names was for George Floyd during the week of May 31 to June 6, the week after his murder. The percent of tweets referencing Black people that were negative decreased by 32% (from 49.33% in November 4–9 to 33.66% in June 1–7) (p < 0.001), but this decline was temporary, lasting just a few weeks. Themes that emerged during the content analysis included discussion of race or racism in positive (14%) or negative (38%) tones, call for action related to racism (18%), and counter movement/arguments against racism-related changes (6%). Conclusion: Although there was a sharp decline in negative Black sentiment and increased public awareness of structural racism and desire for long-lasting social change, these shifts were transitory and returned to baseline after several weeks. Findings suggest that negative attitudes towards Black people remain deeply entrenched.

Background: Our study aimed to describe themes of tweets related to COVID-19 vaccines, race, and ethnicity to explore the context of the intersection of these topics on Twitter. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect a random 1% sample of publicly available tweets from October 2020 to January 2021. The study team conducted a qualitative content analysis from the full data set of 1110 tweets. Results: The tweets revealed vaccine support through vaccine affirmation, advocacy through reproach, a need for a vaccine, COVID-19 and racism, vaccine development and efficacy, racist vaccine humor, and news updates. Vaccine opposition was demonstrated through direct opposition, vaccine hesitancy, and adverse reactions. Conspiracy and misinformation included scientific misinformation, political misinformation, beliefs about immunity and protective behaviors, and race extermination conspiracy. Equity and access focused on overcoming history of medical racism, pointing out health disparities, and facilitators to vaccine access. Representation touted pride in development and role models, and politics discussed the role of politics in vaccines and international politics. Conclusion: Our analysis demonstrates that Twitter can provide nuances about multiple viewpoints on the vaccine related to race and ethnicity and can be beneficial in contributing to insights for public health messaging.

Objectives. Modifiable behaviors during the first 1000 days (conception age 24 months) mediate Hispanic children’s obesity disparities. We aimed to examine underlying reasons for early life obesity risk factors and identify potential early life intervention strategies.Methods. We conducted 7 focus groups with 49 Hispanic women who were pregnant or had children < age 24 months. Domains included influences on childhood obesity risk factors and future intervention ideas. We analyzed data with immersion-crystallization methods until no new themes emerged.Results. Themes included coping with pregnancy may trump healthy eating and physical activity; early life weight gain is unrelated to later life obesity; fear of infant hunger drives bottle and early solids introduction; beliefs about infant taste promote early solids and sugary beverage introduction; and belief that screen time promotes infant development. Mothers identified physicians, nutritionists, and relatives as important health information sources and expressed interest in mobile technology and group or home visits for interventions.Conclusion. Opportunities exist in the first 1000 days to improve Hispanic mothers’ understanding of the role of early life weight gain in childhood obesity and other obesity risk factors. Interventions that link health care and public health systems and include extended family may prevent obesity among Hispanic children.