Public Perception of COVID-19 Vaccines on Twitter in the United States

BackgroundCOVID-19 vaccines play a vital role in combating the COVID-19 pandemic. Social media provides a rich data source to study public perception of COVID-19 vaccines. ObjectiveIn this study, we aimed to examine public perception and discussion of COVID-19 vaccines on Twitter in the US, as well as geographic and demographic characteristics of Twitter users who discussed about COVID-19 vaccines. MethodsThrough Twitter streaming Application Programming Interface (API), COVID-19-related tweets were collected from March 5th, 2020 to January 25th, 2021 using relevant keywords (such as "corona", "covid19", and "covid"). Based on geolocation information provided in tweets and vaccine-related keywords (such as "vaccine" and "vaccination"), we identified COVID-19 vaccine-related tweets from the US. Topic modeling and sentiment analysis were performed to examine public perception and discussion of COVID-19 vaccines. Demographic inference using computer vision algorithm (DeepFace) was performed to infer the demographic characteristics (age, gender and race/ethnicity) of Twitter users who tweeted about COVID-19 vaccines. ResultsOur longitudinal analysis showed that the discussion of COVID-19 vaccines on Twitter in the US reached a peak at the end of 2020. Average sentiment score for COVID-19 vaccine-related tweets remained relatively stable during our study period except for two big peaks, the positive peak corresponds to the optimism about the development of COVID-19 vaccines and the negative peak corresponds to worrying about the availability of COVID-19 vaccines. COVID-19 vaccine-related tweets from east coast states showed relatively high sentiment score. Twitter users from east, west and southern states of the US, as well as male users and users in age group 30-49 years, were more likely to discuss about COVID-19 vaccines on Twitter. ConclusionsPublic discussion and perception of COVID-19 vaccines on Twitter were influenced by the vaccine development and the pandemic, which varied depending on the geographics and demographics of Twitter users.

Mental Health Concerns Related to the COVID-19 Pandemic on Twitter in the UK

BackgroundAmid the COVID-19 pandemic, mental health-related symptoms (such as depression and anxiety) have been actively mentioned on social media. ObjectiveIn this study, we aimed to monitor mental health concerns on Twitter during the COVID-19 pandemic in the United Kingdom (UK), and assess the potential impact of the COVID-19 pandemic on mental health concerns of Twitter users. MethodsWe collected COVID-19 and mental health-related tweets from the UK between March 5, 2020 and January 31, 2021 through the Twitter Streaming API. We conducted topic modeling using Latent Dirichlet Allocation model to examine discussions about mental health concerns. Deep learning algorithms including Face++ were used to infer the demographic characteristics (age and gender) of Twitter users who expressed mental health concerns related to the COVID-19 pandemic. ResultsWe showed a positive correlation between COVID-19-related mental health concerns on Twitter and the severity of the COVID-19 pandemic in the UK. Geographic analysis showed that populated urban areas have a higher proportion of Twitter users with mental health concerns compared to England as a whole. Topic modeling showed that general concerns, COVID-19 skeptics, and Death toll were the top topics discussed in mental health-related tweets. Demographic analysis showed that middle-aged and older adults might be more likely to suffer from mental health issues or express their mental health concerns on Twitter during the COVID-19 pandemic. ConclusionsThe COVID-19 pandemic has noticeable effects on mental health concerns on Twitter in the UK, which varied among demographic and geographic groups.

The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States

BackgroundMental health illness is a growing problem in recent years. During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. ObjectiveIn this study, we aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the United States and infer the demographic composition of Twitter users who had mental health concerns. MethodsCOVID-19 related tweets from March 5th, 2020 to January 31st, 2021 were collected through Twitter streaming API using COVID-19 related keywords (e.g., "corona", "covid19", "covid"). By further filtering using mental health keywords (e.g., "depress", "failure", "hopeless"), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users discussions surrounding mental health concerns. Demographic inference using deep learning algorithms (including Face++ and Ethnicolr) was performed to infer the demographic composition of Twitter users who had mental health concerns during the COVID-19 pandemic. ResultsWe observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that "stay-at-home", "death poll" and "politics and policy" were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns. ConclusionsThe COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males, White) were more likely to have mental health concerns during the COVID-19 pandemic.

Public Reactions towards the COVID-19 Pandemic on Twitter in the United Kingdom and the United States

BackgroundThe coronavirus disease 2019 (COVID-19) has spread globally since December 2019. Twitter is a popular social media platform with active discussions about the COVID-19 pandemic. The public reactions on Twitter about the COVID-19 pandemic in different countries have not been studied. This study aims to compare the public reactions towards the COVID-19 pandemic between the United Kingdom and the United States from March 6, 2020 to April 2, 2020. DataThe numbers of confirmed COVID-19 cases in the United Kingdom and the United States were obtained from the 1Point3Acres website. Twitter data were collected using COVID-19 related keywords from March 6, 2020 to April 2, 2020. MethodsTemporal analyses were performed on COVID-19 related Twitter posts (tweets) during the study period to show daily trends and hourly trends. The sentiment scores of the tweets on COVID-19 were analyzed and associated with the policy announcements and the number of confirmed COVID-19 cases. Topic modeling was conducted to identify related topics discussed with COVID-19 in the United Kingdom and the United States. ResultsThe number of daily new confirmed COVID-19 cases in the United Kingdom was significantly lower than that in the United States during our study period. There were 3,556,442 COVID-19 tweets in the United Kingdom and 16,280,065 tweets in the United States during the study period. The number of COVID-19 tweets per 10,000 Twitter users in the United Kingdom was lower than that in the United States. The sentiment scores of COVID-19 tweets in the United Kingdom were less negative than those in the United States. The topics discussed in COVID-19 tweets in the United Kingdom were mostly about the gratitude to government and health workers, while the topics in the United States were mostly about the global COVID-19 pandemic situation. ConclusionOur study showed correlations between the public reactions towards the COVID-19 pandemic on Twitter and the confirmed COVID-19 cases as well as the policies related to the COVID-19 pandemic in the United Kingdom and the United States.

Public Opinions towards COVID-19 in California and New York on Twitter

BackgroundWith the pandemic of COVID-19 and the release of related policies, discussions about the COVID-19 are widespread online. Social media becomes a reliable source for understanding public opinions toward this virus outbreak. ObjectiveThis study aims to explore public opinions toward COVID-19 on social media by comparing the differences in sentiment changes and discussed topics between California and New York in the United States. MethodsA dataset with COVID-19-related Twitter posts was collected from March 5, 2020 to April 2, 2020 using Twitter streaming API. After removing any posts unrelated to COVID-19, as well as posts that contain promotion and commercial information, two individual datasets were created based on the geolocation tags with tweets, one containing tweets from California state and the other from New York state. Sentiment analysis was conducted to obtain the sentiment score for each COVID-19 tweet. Topic modeling was applied to identify top topics related to COVID-19. ResultsWhile the number of COVID-19 cases increased more rapidly in New York than in California in March 2020, the number of tweets posted has a similar trend over time in both states. COVID-19 tweets from California had more negative sentiment scores than New York. There were some fluctuations in sentiment scores in both states over time, which might correlate with the policy changes and the severity of COVID-19 pandemic. The topic modeling results showed that the popular topics in both California and New York states are similar, with "protective measures" as the most prevalent topic associated with COVID-19 in both states. ConclusionsTwitter users from California had more negative sentiment scores towards COVID-19 than Twitter users from New York. The prevalent topics about COVID-19 discussed in both states were similar with some slight differences.

Health and Demographic Impact on COVID-19 Infection and Mortality in US Counties

IntroductionWith the pandemic of COVID-19, the number of confirmed cases and related deaths are increasing in the US. We aimed to understand the potential impact of health and demographic factors on the infection and mortality rates of COVID-19 at the population level. MethodsWe collected total number of confirmed cases and deaths related to COVID-19 at the county level in the US from January 21, 2020 to April 23, 2020. We extracted health and demographic measures for each US county. Multivariable linear mixed effects models were used to investigate potential correlations of health and demographic characteristics with the infection and mortality rates of COVID-19 in US counties. ResultsOur models showed that several health and demographic factors were positively correlated with the infection rate of COVID-19, such as low education level and percentage of Black. In contrast, several factors, including percentage of smokers and percentage of food insecure, were negatively correlated with the infection rate of COVID-19. While the number of days since first confirmed case and the infection rate of COVID-19 were negatively correlated with the mortality rate of COVID-19, percentage of elders (65 and above) and percentage of rural were positively correlated with the mortality rate of COVID-19. ConclusionsAt the population level, health and demographic factors could impact the infection and mortality rates of COVID-19 in US counties.

Are Vapers More Susceptible to COVID-19 Infection?

BackgroundCOVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was declared a global pandemic in March 2020. Electronic cigarette use (vaping) rapidly gained popularity in the US in recent years. Whether electronic cigarette users (vapers) are more susceptible to COVID-19 infection is unknown. MethodsUsing integrated data in each US state from the 2018 Behavioral Risk Factor Surveillance System (BRFSS), United States Census Bureau and the 1Point3Acres.com website, generalized estimating equation (GEE) models with negative binomial distribution assumption and log link functions were used to examine the association of weighted proportions of vapers with number of COVID-19 infections and deaths in the US. ResultsThe weighted proportion of vapers who used e-cigarettes every day or some days ranged from 2.86% to 6.42% for US states. Statistically significant associations were observed between the weighted proportion of vapers and number of COVID-19 infected cases as well as COVID-19 deaths in the US after adjusting for the weighted proportion of smokers and other significant covariates in the GEE models. With every one percent increase in weighted proportion of vapers in each state, the number of COVID-19 infected cases increase by 0.3139 (95% CI: 0.0554 -0.5723) and the number of COVID-19 deaths increase by 0.3705 (95% CI: 0.0623 - 0.6786) in log scale in each US state. ConclusionsThe positive associations between the proportion of vapers and the number of COVID-19 infected cases and deaths in each US state suggest an increased susceptibility of vapers to COVID-19 infections and deaths.