An analysis of COVID-19 vaccine sentiments and opinions on Twitter

Yousefinaghani, Samira; Dara, Rozita; Mubareka, Samira; Papadopoulos, Andrew; Sharif, Shayan

doi:10.1016/j.ijid.2021.05.059

Cited by 202 publications

(168 citation statements)

References 30 publications

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“…Several studies have assessed the feelings of people both before and after the advent of vaccinations. Snscrape [ 9 ] was used to collect historical tweets about COVID-19 vaccinations from January 7, 2020, to January 3, 2021.In total, 4,552,652 Twitter posts were extracted. These tweets were produced by 1,566,590 users, with 1,012,419 hash tags and 2,258,307 reference terms.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Alam

Khan

Dhruba

et al. 2021

Computational and Mathematical Methods in Medicine

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The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people’s minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Alam

Khan

Dhruba

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Emotion was dominant in 25% of the posts from the anti-vaccination group, whereas emotion was detected in only 0.3% of the posts from the pro-vaccination group (c.f, Germani and Biller-Andorno, 2021 ). Moreover, about 20% of vaccine-related posts were written by anti-vaccine activists (Yousefinaghani et al, 2021 ). We acknowledge that the emotion imprinted in Twitter by the anti-vaccine group may skew the data we used to construct the negative perception of the COVID pandemic.…”

Section: Resultsmentioning

confidence: 99%

Negative Perception of the COVID-19 Pandemic Is Dropping: Evidence From Twitter Posts

et al. 2021

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The COVID-19 pandemic hit hard society, strongly affecting the emotions of the people and wellbeing. It is difficult to measure how the pandemic has affected the sentiment of the people, not to mention how people responded to the dramatic events that took place during the pandemic. This study contributes to this discussion by showing that the negative perception of the people of the COVID-19 pandemic is dropping. By negative perception, we mean the number of negative words the users of Twitter, a social media platform, employ in their online posts. Seen as aggregate, Twitter users are using less and less negative words as the pandemic evolves. The conclusion that the negative perception is dropping comes from a careful analysis we made in the contents of the COVID-19 Twitter chatter dataset, a comprehensive database accounting for more than 1 billion posts generated during the pandemic. We explore why the negativity of the people decreases, making connections with psychological traits such as psychophysical numbing, reappraisal, suppression, and resilience. In particular, we show that the negative perception decreased intensively when the vaccination campaign started in the USA, Canada, and the UK and has remained to decrease steadily since then. This finding led us to conclude that vaccination plays a key role in dropping the negativity of the people, thus promoting their psychological wellbeing.

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“…Finalmente, los estudios métricos que analizan los flujos de información sobre covid-19 en redes sociales han sido muy importantes para caracterizar, por ejemplo, las dinámicas de la desinformación y su relación con disputas de poder e intereses económicos. Análisis de publicaciones e interacciones en Youtube, Facebook, Instagram y Twitter han mostrado las percepciones y sentimientos de los usuarios de estas redes acerca de la vacunación; las disputas y narrativas sobre hidroxicloroquina, ivermectina y azitromicina por parte de autoridades públicas para legitimar posturas populistas y neoliberales; la propagación de teorías de la conspiración, vinculadas a tramas geopolíticas y económicas; y el uso distorsionado de términos y conceptos médicos y científicos en las cadenas de desinformación, entre muchos otros temas (MONARI; SANTOS; SACRAMENTO, 2020;MACHADO et al, 2020;YOUSEFINAGHANI et al, 2021).…”

Section: Los Estudios Métricos De Información Y La Covid-19unclassified