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

Abstract

Background A global rollout of vaccinations is currently underway to mitigate and protect people from the COVID-19 pandemic. Several individuals have been using social media platforms such as Twitter as an outlet to express their feelings, concerns, and opinions about COVID-19 vaccines and vaccination programs. This study examined COVID-19 vaccine–related tweets from January 1, 2020, to April 30, 2021, to uncover the topics, themes, and variations in sentiments of public Twitter users. Objective The aim of this study was to examine key themes and topics from COVID-19 vaccine–related English tweets posted by individuals, and to explore the trends and variations in public opinions and sentiments. Methods We gathered and assessed a corpus of 2.94 million COVID-19 vaccine–related tweets made by 1.2 million individuals. We used CoreX topic modeling to explore the themes and topics underlying the tweets, and used VADER sentiment analysis to compute sentiment scores and examine weekly trends. We also performed qualitative content analysis of the top three topics pertaining to COVID-19 vaccination. Results Topic modeling yielded 16 topics that were grouped into 6 broader themes underlying the COVID-19 vaccination tweets. The most tweeted topic about COVID-19 vaccination was related to vaccination policy, specifically whether vaccines needed to be mandated or optional (13.94%), followed by vaccine hesitancy (12.63%) and postvaccination symptoms and effects (10.44%) Average compound sentiment scores were negative throughout the 16 weeks for the topics postvaccination symptoms and side effects and hoax/conspiracy. However, consistent positive sentiment scores were observed for the topics vaccination disclosure, vaccine efficacy, clinical trials and approvals, affordability, regulation, distribution and shortage, travel, appointment and scheduling, vaccination sites, advocacy, opinion leaders and endorsement, and gratitude toward health care workers. Reversal in sentiment scores in a few weeks was observed for the topics vaccination eligibility and hesitancy. Conclusions Identification of dominant themes, topics, sentiments, and changing trends about COVID-19 vaccination can aid governments and health care agencies to frame appropriate vaccination programs, policies, and rollouts.

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

“…Fourth, this study adopted content analysis and did not incorporate the effects of images or emoticons. In a literature review, we found that researchers removed emojis during preprocessing and cleaning of vaccine message text data to study multiple topics in Twitter, such as online vaccination debates [ 73 ], childhood vaccination opinions [ 74 ], COVID-19 vaccine sentiment in the United States [ 75 ], and key themes and topics on COVID-19 vaccines [ 76 ]. On similar lines of the literature, we removed emojis from the Twitter text corpus to analyze our dissemination model.…”

Analisis Sentimen Terhadap Isu Penundaan Pemilu di Twitter Menggunakan Naive Bayes Clasifier

“…Ada beragam cara untuk melakukan ekstraksi data cuitan twitter, tentunya dengan kelebihan dan kekurangan masingmasing [10]. Dari twitter API [11], tweepy, TWINT, dan snscrape [5], penulis memilih snscrape yang berbasis bahasa python untuk melakukan ekstraksi data cuitan twitter. Penulis melakukan ekstraksi data cuitan twitter berdasarkan dua durasi waktu, yaitu sebelum Muhaimin Iskandar melontarkan isu (1 Januari 2022 sampai dengan 23 Februari 2022) dan setelah Muhaimin Iskandar melontarkan isu (24 Februari 2022 sampai dengan 27 Mei 2022).…”

“…Analisis sentimen terhadap data twitter juga pernah dilakukan untuk melihat sentimen masyarakat di era COVID 19, di antaranya menilai sentimen masyarakat dengan menggunakan VADER (Valence Aware Dictionary and Sentiment Reasoner) terhadap program vaksinasi [5][6] atau bahkan sentimen masyarakat pada saat wabah COVID 19 sedang meninggi [7].…”

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

“…The unprecedented speed of messenger RNA vaccine development and approval has raised concerns that clinical trials were hastened and regulatory standards were relaxed [ 9 ]. Several recent topic modeling studies have reported concerns about AEs as a common major topic [ 10 - 13 ]. Moreover, most countries have used various vaccine brands (eg, Moderna, Pfizer, AstraZeneca, and Jassen), each with different use guidelines and safety and efficacy profiles [ 14 , 15 ].…”

“…We reviewed a rich body of existing literature on topics and sentiments related to COVID-19 vaccines using social media data ( Multimedia Appendix 1 [ 4 - 8 , 10 - 13 , 17 - 30 ]). The major data source was Twitter (21 studies), and other sources were Reddit (2 studies), Facebook (1 study), and Weibo (1 study).…”