Users’ Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts
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Abstract
Within a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France.
Objective
This study aims to identify and investigate the opinions of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis.
Methods
This study was conducted in 2 phases. First, we filtered a collection of tweets related to COVID-19 available on Twitter from February 2020 to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand-labeled subset of 4548 tweets.
Results
A set of 69 relevant keywords were identified as the semantic concept of the word “vaccin” (vaccine in French) and focused mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled us to extract nearly 350,000 tweets in French. The sentiment analysis model achieved 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets, and 43% of neutral tweets. This allowed us to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users’ tweets was that vaccines are perceived as having a political purpose and that COVID-19 is a commercial argument for the pharmaceutical companies.
Conclusions
Twitter might be a useful tool to investigate the arguments for vaccine mistrust because it unveils political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.
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SciScore for 10.1101/2022.02.14.22268832: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: This study focuses on exploiting the textual information of Twitter but doesn’t extract any further metadata such as users’ information. However, a preliminary experience that we conducted earlier showed that the medical professionals seem to be excluded of the debate on social networks, except for a few personalities who are against a potential vaccine. This could lead to a better understanding of the observed dynamics. Another …
SciScore for 10.1101/2022.02.14.22268832: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: This study focuses on exploiting the textual information of Twitter but doesn’t extract any further metadata such as users’ information. However, a preliminary experience that we conducted earlier showed that the medical professionals seem to be excluded of the debate on social networks, except for a few personalities who are against a potential vaccine. This could lead to a better understanding of the observed dynamics. Another limitation relies on the performances of the sentiment analysis. The model could achieve better performances in the near future with better parameters’ optimization and further exploration of other approaches. Models that are unsupervised like zero-shot learning could be interesting for additional investigations. Related work: Mistrust about COVID-19 vaccine has spread widely across social media. Consequently, its influence was able to reach a large part of the population. This mistrust situation was causing concern for health authorities, including the WHO, which lists vaccine mistrust as one of the 10 biggest threats on global health in 2019 next to the threat of a pandemic [35]. According to [36], there are many reasons for this mistrust: One may be doubtful of the vaccine benefit, there may be concerns about long term unexpected side effects, marketing of vaccines may be considered as a mere commercial operation where vendors are profiteering from patients, and one may have a preference for natural immunity rather than getting immunit...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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