Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets

Raghupathi, Viju; Ren, Jie; Raghupathi, Wullianallur

doi:10.3390/ijerph17103464

Cited by 73 publications

(52 citation statements)

References 96 publications

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“…RenderX mixtures (DMM), and k-means of term frequency-inverse document frequency, the primary limitation is the subjectivity in defining the topics created [60,74]. In addition, a sound reason or calculation is needed to support the preset number of topics, which would affect the results.…”

Section: Xsl • Fomentioning

confidence: 99%

Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis

Kwok¹,

Vadde²,

Wang³

2021

J Med Internet Res

176

135

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Background COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity. Objective This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter. Methods We collected 31,100 English tweets containing COVID-19 vaccine–related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia. Results Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion. Conclusions Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines.

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Section: Xsl • Fomentioning

confidence: 99%

Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis

Kwok¹,

Vadde²,

Wang³

2021

J Med Internet Res

176

135

View full text Add to dashboard Cite

show abstract

“…Basic information about Twitter and the definitions of the Twitter terms mentioned in this article can be found in Appendix A . Published articles about opinion analysis towards vaccination on social media usually perform short-term SA [ 9 , 24 , 25 ] in small datasets [ 24 , 26 ]. Some studies are limited to a single location [ 24 , 25 , 27 ], but usually geolocation is not analysed [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Published articles about opinion analysis towards vaccination on social media usually perform short-term SA [ 9 , 24 , 25 ] in small datasets [ 24 , 26 ]. Some studies are limited to a single location [ 24 , 25 , 27 ], but usually geolocation is not analysed [ 3 ]. In our research, we aim to evaluate public perceptions regarding vaccination on Twitter by performing a sentence-level SA on a dataset composed of 1,499,227 vaccine-related tweets, in English and Spanish, published from June 2011 to April 2019.…”

Section: Introductionmentioning

confidence: 99%

Vaccine Hesitancy on Social Media: Sentiment Analysis from June 2011 to April 2019

et al. 2021

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Vaccine hesitancy was one of the ten major threats to global health in 2019, according to the World Health Organisation. Nowadays, social media has an important role in the spread of information, misinformation, and disinformation about vaccines. Monitoring vaccine-related conversations on social media could help us to identify the factors that contribute to vaccine confidence in each historical period and geographical area. We used a hybrid approach to perform an opinion-mining analysis on 1,499,227 vaccine-related tweets published on Twitter from 1st June 2011 to 30th April 2019. Our algorithm classified 69.36% of the tweets as neutral, 21.78% as positive, and 8.86% as negative. The percentage of neutral tweets showed a decreasing tendency, while the proportion of positive and negative tweets increased over time. Peaks in positive tweets were observed every April. The proportion of positive tweets was significantly higher in the middle of the week and decreased during weekends. Negative tweets followed the opposite pattern. Among users with ≥2 tweets, 91.83% had a homogeneous polarised discourse. Positive tweets were more prevalent in Switzerland (71.43%). Negative tweets were most common in the Netherlands (15.53%), Canada (11.32%), Japan (10.74%), and the United States (10.49%). Opinion mining is potentially useful to monitor online vaccine-related concerns and adapt vaccine promotion strategies accordingly.

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“…In this article, such inaccurate or incorrect information is defined as a rumor, which always thrive during crises. From the SARS (severe acute respiratory syndrome) outbreak to the Ebola outbreak and further to the current COVID-19 outbreak, various rumors can quickly spread through a range of media and communication channels that influence people’s risk perception and mislead people’s behaviors [ 3 , 4 , 5 , 6 , 7 ]. During the COVID-19 outbreak, viral dissemination of rumors not only hurt people’s perceptions but also wrecked risk management.…”

Section: Introductionmentioning

confidence: 99%

How the Health Rumor Misleads People’s Perception in a Public Health Emergency: Lessons from a Purchase Craze during the COVID-19 Outbreak in China

Zhang

Chen

et al. 2020

IJERPH

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Health rumors often mislead people and cause adverse health behaviors. Especially during a public health emergency, health rumors may result in severe consequences for people’s health and risk governance. Insight into how these rumors form and harm people’s health behavior is critical for assisting people in establishing scientific health cognition and to enhance public health emergency responses. Using the case study with interview data of a salient purchase craze led by a health rumor during the COVID-19 outbreak in China, this article aimed to illustrate the process of how a piece of information becomes a health rumor. Furthermore, we identify factors that cause people to believe rumors and conduct behavior that leads to a purchase craze. Results show that a public misunderstanding of the unique psychology of uncertainty, cultural and social cognition, and conformity behavior jointly informs people’s beliefs in rumors and further causes purchase craze behavior. We developed a simplified model to demonstrate how an ordinary news report can lead to a rumor. Based on this model, some implications of effective health communication are suggested for managing rumors.

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Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets

Cited by 73 publications

References 96 publications

Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis

Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis

Vaccine Hesitancy on Social Media: Sentiment Analysis from June 2011 to April 2019

How the Health Rumor Misleads People’s Perception in a Public Health Emergency: Lessons from a Purchase Craze during the COVID-19 Outbreak in China

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