Social Media Study of Public Opinions on Potential COVID-19 Vaccines: Informing Dissent, Disparities, and Dissemination

Lyu, Hanjia; Wang, Junda; Wu, Wei; Duong, Viet; Zhang, Xiyang; Dye, Timothy D.; Luo, Jiebo

doi:10.1101/2020.12.12.20248070

Cited by 37 publications

(36 citation statements)

References 29 publications

(34 reference statements)

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“…This can be accomplished by designing effective vaccine-promoting communication by tailoring messages using acquired knowledge about vaccine sentiments and opinions. Opinions for and against COVID-19 vaccination change by community features such as demographics, income, and religious or family status (Lyu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

An analysis of COVID-19 vaccine sentiments and opinions on Twitter

Yousefinaghani

Dara

Mubareka

et al. 2021

International Journal of Infectious Diseases

202

119

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We identified public sentiments and opinions toward the COVID-19 vaccines based on the content of Twitter. Materials and methods: We retrieved 4,552,652 publicly available tweets posted within the timeline of January 2020 to January 2021. Following extraction, we identified vaccine sentiments and opinions of tweets and compared their progression by time, geographical distribution, main themes, keywords, posts engagement metrics and accounts characteristics. Results: We found a slight difference in the prevalence of positive and negative sentiments, with positive being the dominant polarity and having higher engagements. The amount of discussion on vaccine rejection and hesitancy was more than interest in vaccines during the course of the study, but the pattern was different in various countries. We found the accounts producing vaccine opposition content were partly Twitter bots or political activists while well-known individuals and organizations generated the content in favour of vaccination. Conclusion: Understanding sentiments and opinions toward vaccination using Twitter may help public health agencies to increase positive messaging and eliminate opposing messages in order to enhance vaccine uptake.

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Section: Introductionmentioning

confidence: 99%

An analysis of COVID-19 vaccine sentiments and opinions on Twitter

Yousefinaghani

Dara

Mubareka

et al. 2021

International Journal of Infectious Diseases

202

119

View full text Add to dashboard Cite

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“…COVID-19 is no exception. Indeed, using social media, public opinions on personal face mask use [ 19 ] and COVID-19 vaccine uptake [ 20 , 21 ] have been investigated. Existing research has also studied the predictive power of online medical consultation, online medical appointment, and online medical search in forecasting regional outbreaks and found online medical consultation to be the most predicative [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study

Zhang¹,

Lyu²,

Liu³

et al. 2021

JMIR Infodemiology

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Background The COVID-19 pandemic has affected people’s daily lives and has caused economic loss worldwide. Anecdotal evidence suggests that the pandemic has increased depression levels among the population. However, systematic studies of depression detection and monitoring during the pandemic are lacking. Objective This study aims to develop a method to create a large-scale depression user data set in an automatic fashion so that the method is scalable and can be adapted to future events; verify the effectiveness of transformer-based deep learning language models in identifying depression users from their everyday language; examine psychological text features’ importance when used in depression classification; and, finally, use the model for monitoring the fluctuation of depression levels of different groups as the disease propagates. Methods To study this subject, we designed an effective regular expression-based search method and created the largest English Twitter depression data set containing 2575 distinct identified users with depression and their past tweets. To examine the effect of depression on people’s Twitter language, we trained three transformer-based depression classification models on the data set, evaluated their performance with progressively increased training sizes, and compared the model’s tweet chunk-level and user-level performances. Furthermore, inspired by psychological studies, we created a fusion classifier that combines deep learning model scores with psychological text features and users’ demographic information, and investigated these features’ relations to depression signals. Finally, we demonstrated our model’s capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. Results Our fusion model demonstrated an accuracy of 78.9% on a test set containing 446 people, half of which were identified as having depression. Conscientiousness, neuroticism, appearance of first person pronouns, talking about biological processes such as eat and sleep, talking about power, and exhibiting sadness were shown to be important features in depression classification. Further, when used for monitoring the depression trend, our model showed that depressive users, in general, responded to the pandemic later than the control group based on their tweets (n=500). It was also shown that three US states—New York, California, and Florida—shared a similar depression trend as the whole US population (n=9050). When compared to New York and California, people in Florida demonstrated a substantially lower level of depression. Conclusions This study proposes an efficient method that can be used to analyze the depression level of different groups of people on Twitter. We hope this study can raise awareness among researchers and the public of COVID-19’s impact on people’s mental health. The noninvasi...

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“…Tailoring messaging using information about vaccination sentiments and views may lead to a successful vaccine promotion strategy. Some characteristics of the population, including demographics, education, religion, and familial status, influence which opinions are expressed for the COVID-19 vaccine [24].…”

Section: Introductionmentioning

confidence: 99%

Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis

Niu

Liu

Nagai‐Tanima

et al. 2021

Preprint

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Background: The global public health and socioeconomic impacts of COVID-19 have been substantial. To achieve herd immunity, widespread use of the vaccine is required, and it is therefore critical for government and public health agencies to understand public perceptions of the vaccine to help sustain subsequent vaccinations. Objective: This study aims to explore the opinions and sentiments of tweets about COVID-19 vaccination among Twitter users in Japan, both before and at the beginning of the COVID-19 vaccination program. Methods: We collected 144,101 Japanese tweets containing COVID-19 vaccine-related keywords from Japanese Twitter users between August 1, 2020, and June 30, 2021. Specifically, we identified temporal changes in the number of tweets and key events that triggered a surge in the number of tweets. In addition, we performed sentiment analysis, and calculated the correlation between different sentiments and the number of deaths, infections, and vaccinations. We also built latent Dirichlet allocation (LDA) topic models to identify commonly discussed topics in a large sample of tweets. We also provided a word cloud of high-frequency unigram and bigram tokens as additional evidence, and conducted further analysis on three different vaccine brands. Results: The overall number of tweets has continued to increase since the start of mass vaccination in Japan. Sentiments were generally neutral, but negative sentiment was more significant than positive sentiment. Before and after the first vaccination in Japan, the correlations of negative/positive sentiment with death, infection, and vaccination cases changed significantly. Public concerns revolved around three themes: information on vaccine reservations and vaccinations in Japan; infection and mutation of COVID-19 in Japan; and prevention measures, vaccine development and supply, and vaccination status in other countries. Furthermore, public attention to the three brands of vaccines has a temporal shift as clinical trials move forward. Conclusions: The number of tweets and changes in sentiment are primarily driven by major news events in relation to the COVID-19 vaccine, with negative sentiments dominating positive sentiments overall. Death and infection cases correlated significantly with negative sentiments, but the correlation fell after vaccinations began as morbidity and mortality decreased. The attention of the public to different vaccine brands had a temporal change during their clinical trial process, and although the discussion points are slightly different, the core remains effective and secure.

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Social Media Study of Public Opinions on Potential COVID-19 Vaccines: Informing Dissent, Disparities, and Dissemination

Cited by 37 publications

References 29 publications

An analysis of COVID-19 vaccine sentiments and opinions on Twitter

An analysis of COVID-19 vaccine sentiments and opinions on Twitter

Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study

Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis

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