Covid-19 vaccine hesitancy on English-language Twitter

Thelwall, Mike; Kousha, Kayvan; Thelwall, Saheeda

doi:10.3145/epi.2021.mar.12

Cited by 63 publications

(58 citation statements)

References 61 publications

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“…Some topics are issues related to vaccines, their performance, immunity, mechanism, development timeline, side effects, and approval process. While relevant studies focused on few general topics including attitudes [35], vaccine development [35], complaints in Australia [35], and vaccine hesitancy [65] and were developed mostly on tweets posted in 2020 before starting the vaccination in the U.S., this paper identified a wide range of topics posted in four months in 2020 and 2021 and provided more details on issues discussed on Twitter. According to a survey [64], side effects, emergency approval, and effectiveness are the main concerns of people who are reluctant to get the COVID-19 vaccine.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter

et al. 2021

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The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.

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Section: Discussionmentioning

confidence: 99%

COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter

et al. 2021

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“…In the discourse on COVID-19 vaccines, the main issues were that they were developed quickly, and they could compromise safety. Those issues included the fear that vaccines would alter human DNA, cause allergic reactions to vaccine ingredients, result in sudden deaths due to frailty syndrome, or cause infertility [48,49]. Wide anti-COVID immunization programs promulgated a discourse in which risk (e.g., the discomfort of common, but mild AEs as well as rare, but serious AEs) and benefits (e.g., efficacy in protecting from the disease) were described as "tradeoffs" of being vaccinated.…”

Section: Discussionmentioning

confidence: 99%

Mild Adverse Events of Sputnik V Vaccine Extracted from Russian Language Telegram Posts via BERT Deep Learning Model

Jarynowski

Semenov

Kaminski³

et al. 2021

Preprint

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Background: There is a limited amount of data on the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V) safety profile. Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Materials and Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multi-label classifications using the deep neural language model BERT DeepPavlov, which we pre-trained on a Russian language corpus and applied to the Telegram messages. The resulting AUC score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results: The results of the retrospective analysis showed that females reported more AEs than males (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.13-fold, P<.001), and the number of AEs decreased with age (β = .05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) compared with mRNA ones (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase III clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=.94, P=.02) with those reported in the Argentinian post-marketing AE registry. Conclusion: After receiving the Sputnik V vaccination, Telegram users complained about pain (47%), fever (47%), fatigue (34%), and headache (25%). The results showed that the AE profile of Sputnik V was comparable with other COVID-19 vaccines. Examining the sentinel properties of participatory trials (which is subject to self-reporting biases) could still provide meaningful information about pharmaceutics, especially if only a limited amount of information on AEs is provided by producers.

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“…For example, Hussain et al [ 35 ] conducted a study to analyze public sentiments towards COVID-19 vaccines in the US and the UK by extracting information from more than 300,000 posts from Facebook and Twitter collected from 1 March to 22 November 2020. Thelwall et al [ 28 ] used tweets to understand what types of perceptions and attitudes are shared on Twitter so that appropriate actions can be taken to stop the spread of misinformation/disinformation. Clarivate’s social intelligence experts [ 40 ] analyzed tweets to monitor the evolving perspectives of Americans on COVID-19 vaccines.…”

Section: Related Workmentioning

confidence: 99%

“…Public sentiment analysis based on social media data has become the preferred approach to identify and address many trending issues of our society [ 4 , 5 , 28 , 29 , 30 ]. In this research, we analyzed public sentiment vis à vis COVID-19 vaccination using Twitter data (tweets that were posted from across the US).…”

Section: Introductionmentioning

confidence: 99%

Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics

et al. 2021

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There is a compelling and pressing need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February and late March 2021 shows that, despite the overall strength of positive sentiment and despite the increasing numbers of Americans being fully vaccinated, negative sentiment towards COVID-19 vaccines still persists among segments of people who are hesitant towards the vaccine. In this study, we perform sentiment analytics on vaccine tweets, monitor changes in public sentiment over time, contrast vaccination sentiment scores with actual vaccination data from the US CDC and the Household Pulse Survey (HPS), explore the influence of maturity of Twitter user-accounts and generate geographic mapping of tweet sentiments. We observe that fear sentiment remained unchanged in populous states, whereas trust sentiment declined slightly in these same states. Changes in sentiments were more notable among less populous states in the central sections of the US. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework, which was developed for COVID-19 sentiment analytics, to systematically posit implications for public policy processes with the aim of improving the positioning, messaging, and administration of vaccines. These insights are expected to contribute to policies that can expedite the vaccination program and move the nation closer to the cherished herd immunity goal.

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Covid-19 vaccine hesitancy on English-language Twitter

Cited by 63 publications

References 61 publications

COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter

COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter

Mild Adverse Events of Sputnik V Vaccine Extracted from Russian Language Telegram Posts via BERT Deep Learning Model

Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics

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