Sentiment analysis tracking of COVID-19 vaccine through tweets

Sarirete, Akila

doi:10.1007/s12652-022-03805-0

Cited by 22 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we compared our work with 29 significant Twitter studies [ 2 - 7 , 18 , 20 , 21 , 23 - 26 , 119 - 134 ]. Unsupervised methods, such as sentiment analysis and topic modeling, are the most popular methods used to classify and analyze tweets; 11 studies used some combination of sentiment analysis, emotion analysis, and topic modeling [ 3 , 6 , 20 , 21 , 24 , 124 , 126 , 128 , 130 , 131 , 134 ]. Most of these studies analyzed large tweet data sets comprising millions of tweets.…”

Section: Discussionmentioning

confidence: 99%

“…A growing body of literature has analyzed social media posts, particularly tweets related to COVID-19 vaccines. Here, we compared our work with 29 significant Twitter studies [2][3][4][5][6][7]18,20,21,[23][24][25][26][119][120][121][122][123][124][125][126][127][128][129][130][131][132][133][134]. Unsupervised methods, such as sentiment analysis and topic modeling, are the most popular methods used to classify and analyze tweets; 11 studies used some combination of sentiment analysis, emotion analysis, and topic modeling [3,6,20,21,24,124,126,128,130,131,134].…”

Section: Comparison With Prior Workmentioning

confidence: 99%

See 1 more Smart Citation

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

Zaidi¹,

Ye²,

Samon³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the ongoing COVID-19 pandemic but also for future pathogen outbreaks. There are various research efforts in this domain, although, a need still exists for a comprehensive topic-wise analysis of tweets in favor of and against COVID-19 vaccines. Objective This study characterizes the discussion points in favor of and against COVID-19 vaccines posted on Twitter during the first year of the pandemic. The aim of this study was primarily to contrast the views expressed by both camps, their respective activity patterns, and their correlation with vaccine-related events. A further aim was to gauge the genuineness of the concerns expressed in antivax tweets. Methods We examined a Twitter data set containing 75 million English tweets discussing the COVID-19 vaccination from March 2020 to March 2021. We trained a stance detection algorithm using natural language processing techniques to classify tweets as antivax or provax and examined the main topics of discourse using topic modeling techniques. Results Provax tweets (37 million) far outnumbered antivax tweets (10 million) and focused mostly on vaccine development, whereas antivax tweets covered a wide range of topics, including opposition to vaccine mandate and concerns about safety. Although some antivax tweets included genuine concerns, there was a large amount of falsehood. Both stances discussed many of the same topics from opposite viewpoints. Memes and jokes were among the most retweeted messages. Most tweets from both stances (9,007,481/10,566,679, 85.24% antivax and 24,463,708/37,044,507, 66.03% provax tweets) came from dual-stance users who posted both provax and antivax tweets during the observation period. Conclusions This study is a comprehensive account of COVID-19 vaccine discourse in the English language on Twitter from March 2020 to March 2021. The broad range of discussion points covered almost the entire conversation, and their temporal dynamics revealed a significant correlation with COVID-19 vaccine–related events. We did not find any evidence of polarization and prevalence of antivax discourse over Twitter. However, targeted countering of falsehoods is important because only a small fraction of antivax discourse touched on a genuine issue. Future research should examine the role of memes and humor in driving web-based social media activity.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Comparison With Prior Workmentioning

confidence: 99%

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

Zaidi¹,

Ye²,

Samon³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…The authors proposed a machine learningbased approach for sentiment analysis of COVID-19 tweets, using a dataset of 10,000 tweets that were manually annotated for the sentiment. The authors compared the performance of several machine learning algorithms, including Naive Bayes, Support Vector Machines (SVM), and Random Forest, and found that SVM achieved the best performance with an accuracy of 85.7% [15].…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

Rukhsar¹,

Awan²,

Naseem³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions, sentiments, and data in the modern era. Twitter, a widely used microblogging site where individuals share their thoughts in the form of tweets, has become a major source for sentiment analysis. In recent years, there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets. Opinions or expressions of people about a particular topic, situation, person, or product can be identified from sentences and divided into three categories: positive for good, negative for bad, and neutral for mixed or confusing opinions. The process of analyzing changes in sentiment and the combination of these categories is known as "sentiment analysis." In this study, sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning methods. The deep learning-based model long-short-term memory (LSTM) performed better than machine learning approaches. Long short-term memory achieved 87% accuracy, and the support vector machine (SVM) classifier achieved slightly worse results than LSTM at 86%. The study also tested binary classes of positive and negative, where LSTM and SVM both achieved 90% accuracy.

show abstract

“…It is commonly used in business to establish consumer trends, such as through customer reviews [37,38]. It has been previously used in healthcare to analyze sentiments surrounding patient experience [39] and COVID-19 vaccine hesitancy [40,41]. However, the use of sentiment analysis in healthcare is lacking, perhaps in part due to insu cient domain-speci c textual data [42].…”

Section: Sentiment Analysis Of Personal Experiencementioning

confidence: 99%

Patient and family experience in pediatric spine surgery: a social media analysis

Levett

Elkaim

Weber

et al. 2022

Preprint

View full text Add to dashboard Cite

There is little data on patient and caregiver perceptions of spine surgery in children and youth. This study aims to characterize the personal experiences of patients, caregivers, and family members surrounding pediatric spine surgery through a qualitative and quantitative social media analysis. The Twitter application programming interface was searched for keywords related to pediatric spine surgery from inception to March 2022. Relevant tweets and accounts were extracted and subsequently classified using thematic labels. Tweet metadata was collected to measure user engagement via multivariable regression. Sentiment analysis using Natural Language Processing was performed on all tweets with a focus on tweets discussing the personal experiences of patients and caregivers. 2424 tweets from 1847 individual accounts were retrieved for analysis. Patients and caregivers represented 1459 (79.0%) of all accounts. Posts discussed the personal experiences of patients and caregivers in 83.5% of tweets. Pediatric spine surgery research was discussed in a few posts (n=90, 3.7%). Within the personal experience category, 975 (48.17%) tweets were positive, 516 (25.49%) were negative, and 533 (26.34%) were neutral. Presence of a tag (beta: -6.1, 95% CI -9.7 to -2.5) and baseline follower count (beta<0.001, 95% CI <0.001 to <0.001) significantly affected tweet engagement negatively and positively, respectively. Patients and caregivers actively discuss topics related to pediatric spine surgery on Twitter. Posts discussing personal experience are most prevalent, while posts on research are scarce, unlike previous social media studies. Pediatric spine surgeons can leverage this dialogue to better understand the worries and needs of patients and their families.

show abstract

Sentiment analysis tracking of COVID-19 vaccine through tweets

Cited by 22 publications

References 26 publications

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

Patient and family experience in pediatric spine surgery: a social media analysis

Contact Info

Product

Resources

About