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      Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

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          Abstract

          Background

          As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter.

          Objective

          The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media.

          Methods

          To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity.

          Results

          After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy.

          Conclusions

          This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.

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          Most cited references69

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          Learning the parts of objects by non-negative matrix factorization.

          Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.
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            The COVID-19 social media infodemic

            We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document} R 0 for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors’ amplification.
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              Social media and vaccine hesitancy

              Background Understanding the threat posed by anti-vaccination efforts on social media is critically important with the forth coming need for world wide COVID-19 vaccination programs. We globally evaluate the effect of social media and online foreign disinformation campaigns on vaccination rates and attitudes towards vaccine safety. Methods Weuse a large-n cross-country regression framework to evaluate the effect ofsocial media on vaccine hesitancy globally. To do so, we operationalize social media usage in two dimensions: the use of it by the public to organize action(using Digital Society Project indicators), and the level of negative lyoriented discourse about vaccines on social media (using a data set of all geocoded tweets in the world from 2018-2019). In addition, we measure the level of foreign-sourced coordinated disinformation operations on social media ineach country (using Digital Society Project indicators). The outcome of vaccine hesitancy is measured in two ways. First, we use polls of what proportion ofthe public per country feels vaccines are unsafe (using Wellcome Global Monitor indicators for 137 countries). Second, we use annual data of actual vaccination rates from the WHO for 166 countries. Results We found the use of social media to organise offline action to be highly predictive of the belief that vaccinations are unsafe, with such beliefs mounting as more organisation occurs on social media. In addition, the prevalence of foreign disinformation is highly statistically and substantively significant in predicting a drop in mean vaccination coverage over time. A 1-point shift upwards in the 5-point disinformation scale is associated with a 2-percentage point drop in mean vaccination coverage year over year. We also found support for the connection of foreign disinformation with negative social media activity about vaccination. The substantive effect of foreign disinformation is to increase the number of negative vaccine tweets by 15% for the median country. Conclusion There is a significant relationship between organisation on social media and public doubts of vaccine safety. In addition, there is a substantial relationship between foreign disinformation campaigns and declining vaccination coverage.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                October 2021
                21 October 2021
                21 October 2021
                : 23
                : 10
                : e30765
                Affiliations
                [1 ] College of Computing and Informatics Drexel University Philadelphia, PA United States
                [2 ] Department of Information Management National Sun Yat-sen University Kaohsiung Taiwan
                [3 ] Virtua Voorhees Hospital Voorhees Township, NJ United States
                Author notes
                Corresponding Author: Christopher C Yang chris.yang@ 123456drexel.edu
                Author information
                https://orcid.org/0000-0002-6222-4822
                https://orcid.org/0000-0001-9116-8244
                https://orcid.org/0000-0001-6885-1264
                https://orcid.org/0000-0002-3077-1434
                https://orcid.org/0000-0001-5463-6926
                Article
                v23i10e30765
                10.2196/30765
                8534488
                34581682
                892f6e28-1888-46ef-8ef9-f56f2f68163a
                ©Michal Monselise, Chia-Hsuan Chang, Gustavo Ferreira, Rita Yang, Christopher C Yang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 21.10.2021.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 27 May 2021
                : 30 August 2021
                : 17 September 2021
                : 17 September 2021
                Categories
                Original Paper
                Original Paper

                Medicine
                health care informatics,topic detection,unsupervised sentiment analysis,covid-19,vaccine hesitancy,sentiment,concern,vaccine,social media,trend,trust,health information,twitter,discussion,communication,hesitancy,emotion,fear

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