Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts

Conspiracy narratives and vaccine hesitancy: a scoping review of prevalence, impact, and interventions

Believing conspiracy narratives is frequently assumed to be a major cause of vaccine hesitancy, i.e., the tendency to forgo vaccination despite its availability. In this scoping review, we synthesise and critically evaluate studies that assess i) the occurrence of vaccine-related conspiracy narratives on the internet, ii) the prevalence of belief in vaccine-related conspiracy narratives, iii) the relationship between belief in conspiracy narratives and vaccination intention or vaccination uptake, and iv) interventions that reduce the impact of conspiracy narratives on vaccination intention.In July 2022, we conducted a literature search using three databases: PubMed, PsychInfo, and Web of Science. Following the PRISMA approach, of the 500 initially identified articles, 205 were eligible and analysed.The majority of identified studies were conducted in Europe and North America, were published in 2021 and 2022, and investigated conspiracy narratives around the COVID-19 vaccination. The prevalence of belief in various vaccine-related conspiracy narratives varied greatly across studies, from 2 to 77%. We identified seven experimental studies investigating the effect of exposure to conspiracy narratives on vaccination intentions, of which six indicated a small negative effect. These findings are complemented by the evidence from over 100 correlative studies showing a significant negative relationship between conspiracy beliefs and vaccination intention or uptake. Additionally, the review identified interventions (e.g., social norm feedback, fact-checking labels, or prebunking) that decreased beliefs in vaccine-related conspiracy narratives and, in some cases, also increased vaccination intentions. Yet, these interventions had only small effects.In summary, the review revealed that vaccine-related conspiracy narratives have spread to varying degrees and can influence vaccination decisions. Causal relationships between conspiracy beliefs and vaccination intentions remain underexplored. Further, the review identified a need for more research on interventions that can reduce the impact of conspiracy narratives.

Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study

BACKGROUND

Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC's social media communications and the actual epidemic metrics to improve public health agencies' communication strategies during health emergencies.

OBJECTIVE

This study aimed to identify key topics in tweets posted by the CDC during the pandemic, investigate the temporal dynamics between these key topics and the actual COVID-19 epidemic measures, and make recommendations for the CDC's digital health communication strategies for future health emergencies.

METHODS

Two types of data were collected: (1) a total of 17,524 COVID-19-related English tweets posted by the CDC between December 7, 2019, and January 15, 2022, and (2) COVID-19 epidemic measures in the United States from the public GitHub repository of Johns Hopkins University from January 2020 to July 2022. Latent Dirichlet allocation topic modeling was applied to identify key topics from all COVID-19-related tweets posted by the CDC, and the final topics were determined by domain experts. Various multivariate time series analysis techniques were applied between each of the identified key topics and actual COVID-19 epidemic measures to quantify the dynamic associations between these 2 types of time series data.

RESULTS

Four major topics from the CDC's COVID-19 tweets were identified: (1) information on the prevention of health outcomes of COVID-19; (2) pediatric intervention and family safety; (3) updates of the epidemic situation of COVID-19; and (4) research and community engagement to curb COVID-19. Multivariate analyses showed that there were significant variabilities of progression between the CDC's topics and the actual COVID-19 epidemic measures. Some CDC topics showed substantial associations with the COVID-19 measures over different time spans throughout the pandemic, expressing similar temporal dynamics between these 2 types of time series data.

CONCLUSIONS

Our study is the first to comprehensively investigate the dynamic associations between topics discussed by the CDC on Twitter and the COVID-19 epidemic measures in the United States. We identified 4 major topic themes via topic modeling and explored how each of these topics was associated with each major epidemic measure by performing various multivariate time series analyses. We recommend that it is critical for public health agencies, such as the CDC, to update and disseminate timely and accurate information to the public and align major topics with key epidemic measures over time. We suggest that social media can help public health agencies to inform the public on health emergencies and to mitigate them effectively.

Do Patients Assess Physicians Differently in Video Visits as Compared with In-Person Visits? Insights from Text-Mining Online Physician Reviews

Introduction: Use of both in-person and video visits have become a common norm in health care delivery, especially after the COVID-19 pandemic. It is imperative to understand how patients feel about their providers and their experiences during in-person and video visits. This study examines the important factors that patients use in their reviews and differences in the relative importance. Methods: We performed sentiment analysis and topic modeling on online physician reviews from April 2020 to April 2022. Our dataset comprised 34,824 reviews posted by patients after completing in-person or video visits. Results: Sentiment analysis yielded 27,507 (92.69%) positive and 2,168 (7.31%) negative reviews for in-person visits, and 4,610 (89.53%) positive and 539 (10.47%) negative reviews for video visits. Topic modeling identified seven factors patients used in their reviews: Bedside manners, Medical Expertise, Communication, Visit Environment, Scheduling and Follow-up, Wait times, and Costs and insurance. Patients who gave positive reviews after in-person consultations more frequently mentioned communication, office environment and staff, and bedside manners. Those who gave negative reviews after in-person visits mentioned longer wait times, providers' office and staff, medical expertise, and costs and insurance problems. Patients with positive reviews after video visits emphasized communication, bedside manners, and medical expertise. However, patients posting negative reviews after video visits frequently mentioned problems with appointment scheduling and follow-up, medical expertise, wait times, costs and insurance, and technical problems in video visits. Conclusions: This study identified key factors that influence patients' assessment of their providers in in-person and video visits. Paying attention to these factors can help improve the overall patient experience.

Investigating the Role of Nutrition in Enhancing Immunity During the COVID-19 Pandemic: Twitter Text-Mining Analysis

BACKGROUND

The COVID-19 pandemic has brought to the spotlight the critical role played by a balanced and healthy diet in bolstering the human immune system. There is burgeoning interest in nutrition-related information on social media platforms like Twitter. There is a critical need to assess and understand public opinion, attitudes, and sentiments toward nutrition-related information shared on Twitter.

OBJECTIVE

This study uses text mining to analyze nutrition-related messages on Twitter to identify and analyze how the general public perceives various food groups and diets for improving immunity to the SARS-CoV-2 virus.

METHODS

We gathered 71,178 nutrition-related tweets that were posted between January 01, 2020, and September 30, 2020. The Correlated Explanation text mining algorithm was used to identify frequently discussed topics that users mentioned as contributing to immunity building against SARS-CoV-2. We assessed the relative importance of these topics and performed a sentiment analysis. We also qualitatively examined the tweets to gain a closer understanding of nutrition-related topics and food groups.

RESULTS

Text-mining yielded 10 topics that users discussed frequently on Twitter, viz proteins, whole grains, fruits, vegetables, dairy-related, spices and herbs, fluids, supplements, avoidable foods, and specialty diets. Supplements were the most frequently discussed topic (23,913/71,178, 33.6%) with a higher proportion (20,935/23,913, 87.75%) exhibiting a positive sentiment with a score of 0.41. Consuming fluids (17,685/71,178, 24.85%) and fruits (14,807/71,178, 20.80%) were the second and third most frequent topics with favorable, positive sentiments. Spices and herbs (8719/71,178, 12.25%) and avoidable foods (8619/71,178, 12.11%) were also frequently discussed. Negative sentiments were observed for a higher proportion of avoidable foods (7627/8619, 84.31%) with a sentiment score of -0.39.

CONCLUSIONS

This study identified 10 important food groups and associated sentiments that users discussed as a means to improve immunity. Our findings can help dieticians and nutritionists to frame appropriate interventions and diet programs.

Face time with physicians: How do patients assess providers in video-visits?

Introduction

The COVID-19 pandemic has triggered a massive acceleration in the use of virtual and video-visits. As more patients and providers engage in video-visits over varied digital platforms, it is important to understand how patients assess their providers and the video-visit experiences. We also need to examine the relative importance of the factors that patients use in their assessment of video-visits in order to improve the overall healthcare experience and delivery.

Methods

A data set of 5149 reviews of patients completing a video-visit was assembled through web scraping. Sentiment analysis was performed on the reviews and topic modeling was used to extract latent topics embedded in the reviews and their relative importance.

Results

Most patient reviews (89.53%) reported a positive sentiment towards their providers in video-visits. Seven distinct topics underlying the reviews were identified: bedside manners, professional expertise, virtual experience, appointment scheduling and follow-up process, wait times, costs, and communication. Communication, bedside manners and professional expertise were the top factors patients alluded to in the positive reviews. Appointment-scheduling and follow-ups, wait-times, costs, virtual experience and professional expertise were important factors in the negative reviews.

Discussion

To improve the overall experience of patients in video-visits, providers need to engage in clear communication, grow excellent bedside and webside manners, promptly attend the video-visit with minimal delays and follow-up with patients after the visit.

Users' Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts

BACKGROUND

Within a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France.

OBJECTIVE

This study aims to identify and investigate the opinions of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis.

METHODS

This study was conducted in 2 phases. First, we filtered a collection of tweets related to COVID-19 available on Twitter from February 2020 to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand-labeled subset of 4548 tweets.

RESULTS

A set of 69 relevant keywords were identified as the semantic concept of the word "vaccin" (vaccine in French) and focused mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled us to extract nearly 350,000 tweets in French. The sentiment analysis model achieved 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets, and 43% of neutral tweets. This allowed us to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users' tweets was that vaccines are perceived as having a political purpose and that COVID-19 is a commercial argument for the pharmaceutical companies.

CONCLUSIONS

Twitter might be a useful tool to investigate the arguments for vaccine mistrust because it unveils political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.

A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis

BACKGROUND

The unprecedented speed of COVID-19 vaccine development and approval has raised public concern about its safety. However, studies on public discourses and opinions on social media focusing on adverse events (AEs) related to COVID-19 vaccine are rare.

OBJECTIVE

This study aimed to analyze Korean tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, Janssen, and Novavax) after the vaccine rollout, explore the topics and sentiments of tweets regarding COVID-19 vaccines, and examine their changes over time. We also analyzed topics and sentiments focused on AEs related to vaccination using only tweets with terms about AEs.

METHODS

We devised a sophisticated methodology consisting of 5 steps: keyword search on Twitter, data collection, data preprocessing, data analysis, and result visualization. We used the Twitter Representational State Transfer application programming interface for data collection. A total of 1,659,158 tweets were collected from February 1, 2021, to March 31, 2022. Finally, 165,984 data points were analyzed after excluding retweets, news, official announcements, advertisements, duplicates, and tweets with <2 words. We applied a variety of preprocessing techniques that are suitable for the Korean language. We ran a suite of analyses using various Python packages, such as latent Dirichlet allocation, hierarchical latent Dirichlet allocation, and sentiment analysis.

RESULTS

The topics related to COVID-19 vaccines have a very large spectrum, including vaccine-related AEs, emotional reactions to vaccination, vaccine development and supply, and government vaccination policies. Among them, the top major topic was AEs related to COVID-19 vaccination. The AEs ranged from the adverse reactions listed in the safety profile (eg, myalgia, fever, fatigue, injection site pain, myocarditis or pericarditis, and thrombosis) to unlisted reactions (eg, irregular menstruation, changes in appetite and sleep, leukemia, and deaths). Our results showed a notable difference in the topics for each vaccine brand. The topics pertaining to the Pfizer vaccine mainly mentioned AEs. Negative public opinion has prevailed since the early stages of vaccination. In the sentiment analysis based on vaccine brand, the topics related to the Pfizer vaccine expressed the strongest negative sentiment.

CONCLUSIONS

Considering the discrepancy between academic evidence and public opinions related to COVID-19 vaccination, the government should provide accurate information and education. Furthermore, our study suggests the need for management to correct the misinformation related to vaccine-related AEs, especially those affecting negative sentiments. This study provides valuable insights into the public discourses and opinions regarding COVID-19 vaccination.

The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model

Background

Messages on one's stance toward vaccination on microblogging sites may affect the reader's decision on whether to receive a vaccine. Understanding the dissemination of provaccine and antivaccine messages relating to COVID-19 on social media is crucial; however, studies on this topic have remained limited.

Objective

This study applies the elaboration likelihood model (ELM) to explore the characteristics of vaccine stance messages that may appeal to Twitter users. First, we examined the associations between the characteristics of vaccine stance tweets and the likelihood and number of retweets. Second, we identified the relative importance of the central and peripheral routes in decision-making on sharing a message.

Methods

English-language tweets from the United States that contained provaccine and antivaccine hashtags (N=150,338) were analyzed between April 26 and August 26, 2021. Logistic and generalized negative binomial regressions were conducted to predict retweet outcomes. The content-related central-route predictors were measured using the numbers of hashtags and mentions, emotional valence, emotional intensity, and concreteness. The content-unrelated peripheral-route predictors were measured using the numbers of likes and followers and whether the source was a verified user.

Results

Content-related characteristics played a prominent role in shaping decisions regarding whether to retweet antivaccine messages. Particularly, positive valence (incidence rate ratio [IRR]=1.32, P=.03) and concreteness (odds ratio [OR]=1.17, P=.01) were associated with higher numbers and likelihood of retweets of antivaccine messages, respectively; emotional intensity (subjectivity) was associated with fewer retweets of antivaccine messages (OR=0.78, P=.03; IRR=0.80, P=.04). However, these factors had either no or only small effects on the sharing of provaccine tweets. Retweets of provaccine messages were primarily determined by content-unrelated characteristics, such as the numbers of likes (OR=2.55, IRR=2.24, P<.001) and followers (OR=1.31, IRR=1.28, P<.001).

Conclusions

The dissemination of antivaccine messages is associated with both content-related and content-unrelated characteristics. By contrast, the dissemination of provaccine messages is primarily driven by content-unrelated characteristics. These findings signify the importance of leveraging the peripheral route to promote the dissemination of provaccine messages. Because antivaccine tweets with positive emotions, objective content, and concrete words are more likely to be disseminated, policymakers should pay attention to antivaccine messages with such characteristics.