Understanding Behavioral Intentions Toward COVID-19 Vaccines: Theory-Based Content Analysis of Tweets

Liu, Siru; Liu, Jialin

doi:10.2196/28118

Cited by 35 publications

(38 citation statements)

References 37 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We therefore wanted to evaluate the public's initial reaction and response to COVID-19 vaccination. Few prior studies have included this time frame during the analysis period [30,[34][35][36].…”

Section: Study Datamentioning

confidence: 99%

Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study

Boucher¹,

Cornelson²,

Benham³

et al. 2021

JMIR Infodemiology

View full text Add to dashboard Cite

Background The rollout of COVID-19 vaccines has brought vaccine hesitancy to the forefront in managing this pandemic. COVID-19 vaccine hesitancy is fundamentally different from that of other vaccines due to the new technologies being used, rapid development, and widespread global distribution. Attitudes on vaccines are largely driven by online information, particularly information on social media. The first step toward influencing attitudes about immunization is understanding the current patterns of communication that characterize the immunization debate on social media platforms. Objective We aimed to evaluate societal attitudes, communication trends, and barriers to COVID-19 vaccine uptake through social media content analysis to inform communication strategies promoting vaccine acceptance. Methods Social network analysis (SNA) and unsupervised machine learning were used to characterize COVID-19 vaccine content on Twitter globally. Tweets published in English and French were collected through the Twitter application programming interface between November 19 and 26, 2020, just following the announcement of initial COVID-19 vaccine trials. SNA was used to identify social media clusters expressing mistrustful opinions on COVID-19 vaccination. Based on the SNA results, an unsupervised machine learning approach to natural language processing using a sentence-level algorithm transfer function to detect semantic textual similarity was performed in order to identify the main themes of vaccine hesitancy. Results The tweets (n=636,516) identified that the main themes driving the vaccine hesitancy conversation were concerns of safety, efficacy, and freedom, and mistrust in institutions (either the government or multinational corporations). A main theme was the safety and efficacy of mRNA technology and side effects. The conversation around efficacy was that vaccines were unlikely to completely rid the population of COVID-19, polymerase chain reaction testing is flawed, and there is no indication of long-term T-cell immunity for COVID-19. Nearly one-third (45,628/146,191, 31.2%) of the conversations on COVID-19 vaccine hesitancy clusters expressed concerns for freedom or mistrust of institutions (either the government or multinational corporations) and nearly a quarter (34,756/146,191, 23.8%) expressed criticism toward the government’s handling of the pandemic. Conclusions Social media content analysis combined with social network analysis provides insights into the themes of the vaccination conversation on Twitter. The themes of safety, efficacy, and trust in institutions will need to be considered, as targeted outreach programs and intervention strategies are deployed on Twitter to improve the uptake of COVID-19 vaccination.

show abstract

Section: Study Datamentioning

confidence: 99%

Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study

Boucher¹,

Cornelson²,

Benham³

et al. 2021

JMIR Infodemiology

View full text Add to dashboard Cite

show abstract

“…These were referred to as Protection and Insurance. A similar type of topic classification, not into determinants but overriding themes, was done in the works [47], [52] and [49]. There are many approaches for extracting knowledge from a short text (tweets).…”

mentioning

confidence: 99%

“…Next, an iterative process of topic labeling was performed: (i) topics were labeled to create the first version of labels based on the keywords with the greatest weight, (ii) names of labels were polished through in-depth reading of the most representative topic tweets, and (iii) the final set of topic labels was created. Similar to [47], [49] and [52], our thematic approach relied on human interpretation. Thus, this approach could be significantly influenced by personal understanding of the topics and a variety of biases.…”

mentioning

confidence: 99%

Mapping of the Covid-19 Vaccine Uptake Determinants From Mining Twitter Data

Baj-Rogowska

2021

IEEE Access

View full text Add to dashboard Cite

Opinion polls on vaccine uptake clearly show that Covid-19 vaccine hesitancy is increasing worldwide. Thus, reaching herd immunity not only depends on the efficacy of the vaccine itself, but also on overcoming this hesitancy of uptake in the population. In this study, we revealed the determinants regarding vaccination directly from people's opinions on Twitter, based on the framework of the 6As taxonomy. Covid-19 vaccine acceptance depends mostly on the characteristics of new vaccines (i.e. their safety, side effects, effectiveness, etc.), and the national vaccination strategy (i.e. immunization schedules, quantities of vaccination points and their localization, etc.), which should focus on increasing citizens' awareness, among various other factors. The results of this study point to areas for potentially improving mass campaigns of Covid-19 immunization to increase vaccine uptake and its coverage and also provide insight into possible directions of future research.

show abstract

“…For future work, we will perform a theory-based content analysis to gain insight into the reasons that led to the changes in behavioral intentions we noted in the temporal analysis. Using the transfer learning model in this study, researchers can automatically collect tweets containing COVID-19 vaccine–related behavioral intentions and systematically analyze the data through a theoretical model (eg, Capability, Opportunity, Motivation, Behavior model [ 12 , 46 ]) to promote timely promotion strategies. In addition, researchers can extract individual characteristics from the user profile and perform statistical analysis to determine the relationship between individual characteristics and their behavioral intention toward COVID-19 vaccines.…”

Section: Discussionmentioning

confidence: 99%

“…The SAGA Working Group developed the Vaccine Hesitancy Determinant Matrix, including contextual influences (ie, related to historic, sociocultural, environmental, institutional, economic, or political factors), individual and group influences (ie, factors related to personal perception or social environment), and vaccine/vaccination-specific issues [ 10 ]. Unlike other common vaccines, the COVID-19 vaccines are associated with many factors that might amplify vaccine hesitancy [ 11 , 12 ]. Previous studies have reported widespread public concern about the rapid speed of vaccine development, novelty of the development technology (mRNA), unknown long-term side effects, and politicization of vaccines [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis

Liu¹,

Li²,

Liu³

2021

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

Background The COVID-19 vaccine is considered to be the most promising approach to alleviate the pandemic. However, in recent surveys, acceptance of the COVID-19 vaccine has been low. To design more effective outreach interventions, there is an urgent need to understand public perceptions of COVID-19 vaccines. Objective Our objective was to analyze the potential of leveraging transfer learning to detect tweets containing opinions, attitudes, and behavioral intentions toward COVID-19 vaccines, and to explore temporal trends as well as automatically extract topics across a large number of tweets. Methods We developed machine learning and transfer learning models to classify tweets, followed by temporal analysis and topic modeling on a dataset of COVID-19 vaccine–related tweets posted from November 1, 2020 to January 31, 2021. We used the F1 values as the primary outcome to compare the performance of machine learning and transfer learning models. The statistical values and P values from the Augmented Dickey-Fuller test were used to assess whether users’ perceptions changed over time. The main topics in tweets were extracted by latent Dirichlet allocation analysis. Results We collected 2,678,372 tweets related to COVID-19 vaccines from 841,978 unique users and annotated 5000 tweets. The F1 values of transfer learning models were 0.792 (95% CI 0.789-0.795), 0.578 (95% CI 0.572-0.584), and 0.614 (95% CI 0.606-0.622) for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. Specifically, tweets containing positive behavioral intentions increased significantly in December 2020. In addition, we selected tweets in the following categories: positive attitudes, negative attitudes, positive behavioral intentions, and negative behavioral intentions. We then identified 10 main topics and relevant terms for each category. Conclusions Overall, we provided a method to automatically analyze the public understanding of COVID-19 vaccines from real-time data in social media, which can be used to tailor educational programs and other interventions to effectively promote the public acceptance of COVID-19 vaccines.

show abstract

Understanding Behavioral Intentions Toward COVID-19 Vaccines: Theory-Based Content Analysis of Tweets

Cited by 35 publications

References 37 publications

Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study

Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study

Mapping of the Covid-19 Vaccine Uptake Determinants From Mining Twitter Data

Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis

Contact Info

Product

Resources

About