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      Analysis of individual characteristics influencing user polarization in COVID-19 vaccine hesitancy.

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          Abstract

          During the COVID-19 pandemic, vaccine hesitancy proved to be a major obstacle in efforts to control and mitigate the negative consequences of COVID-19. This study centered on the degree of polarization on social media about vaccine use and contributing factors to vaccine hesitancy among social media users. Examining the discussion about COVID-19 vaccine on the Weibo platform, a relatively comprehensive system of user features was constructed based on psychological theories and models such as the curiosity-drive theory and the big five model of personality. Then machine learning methods were used to explore the paramount impacting factors that led users into polarization. Findings revealed that factors reflecting the activity and effectiveness of social media use promoted user polarization. In contrast, features reflecting users' information processing ability and personal qualities had a negative impact on polarization. This study hopes to help healthcare organizations and governments understand and curb social media polarization around vaccine development in the face of future surges of pandemics.

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          Vaccine hesitancy: Definition, scope and determinants.

          Noni MacDonald (2015)
          The SAGE Working Group on Vaccine Hesitancy concluded that vaccine hesitancy refers to delay in acceptance or refusal of vaccination despite availability of vaccination services. Vaccine hesitancy is complex and context specific, varying across time, place and vaccines. It is influenced by factors such as complacency, convenience and confidence. The Working Group retained the term 'vaccine' rather than 'vaccination' hesitancy, although the latter more correctly implies the broader range of immunization concerns, as vaccine hesitancy is the more commonly used term. While high levels of hesitancy lead to low vaccine demand, low levels of hesitancy do not necessarily mean high vaccine demand. The Vaccine Hesitancy Determinants Matrix displays the factors influencing the behavioral decision to accept, delay or reject some or all vaccines under three categories: contextual, individual and group, and vaccine/vaccination-specific influences.
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            A Unified Approach to Interpreting Model Predictions

            Scott R. Lundberg, Su-In Lee (2017)
            Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches. To appear in NIPS 2017
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              COVID-19 Vaccine Hesitancy Worldwide: A Concise Systematic Review of Vaccine Acceptance Rates

              Malik Sallam (2021)
              Utility of vaccine campaigns to control coronavirus 2019 disease (COVID-19) is not merely dependent on vaccine efficacy and safety. Vaccine acceptance among the general public and healthcare workers appears to have a decisive role in the successful control of the pandemic. The aim of this review was to provide an up-to-date assessment of COVID-19 vaccination acceptance rates worldwide. A systematic search of the peer-reviewed English survey literature indexed in PubMed was done on 25 December 2020. Results from 31 peer-reviewed published studies met the inclusion criteria and formed the basis for the final COVID-19 vaccine acceptance estimates. Survey studies on COVID-19 vaccine acceptance rates were found from 33 different countries. Among adults representing the general public, the highest COVID-19 vaccine acceptance rates were found in Ecuador (97.0%), Malaysia (94.3%), Indonesia (93.3%) and China (91.3%). However, the lowest COVID-19 vaccine acceptance rates were found in Kuwait (23.6%), Jordan (28.4%), Italy (53.7), Russia (54.9%), Poland (56.3%), US (56.9%), and France (58.9%). Only eight surveys among healthcare workers (doctors and nurses) were found, with vaccine acceptance rates ranging from 27.7% in the Democratic Republic of the Congo to 78.1% in Israel. In the majority of survey studies among the general public stratified per country (29/47, 62%), the acceptance of COVID-19 vaccination showed a level of ≥70%. Low rates of COVID-19 vaccine acceptance were reported in the Middle East, Russia, Africa and several European countries. This could represent a major problem in the global efforts to control the current COVID-19 pandemic. More studies are recommended to address the scope of COVID-19 vaccine hesitancy. Such studies are particularly needed in the Middle East and North Africa, Sub-Saharan Africa, Eastern Europe, Central Asia, Middle and South America. Addressing the scope of COVID-19 vaccine hesitancy in various countries is recommended as an initial step for building trust in COVID-19 vaccination efforts.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Comput Human Behav
                Journal ID (iso-abbrev): Comput Human Behav
                Title: Computers in Human Behavior
                Publisher: Published by Elsevier Ltd.
                ISSN (Print): 0747-5632
                ISSN (Electronic): 0747-5632
                Publication date PMC-release: 17 January 2023
                Publication date (Electronic): 17 January 2023
                Electronic Location Identifier: 107649
                Affiliations
                [a ]School of Information Management, Wuhan University, Wuhan, 430072, China
                [b ]School of Data Science, City University of Hong Kong, Hong Kong, 999077, China
                [c ]Center for Studies of Information Resources, Wuhan University, Wuhan, 430072, China
                [d ]Big Data Institute, Wuhan University, Wuhan, 430072, China
                Author notes
                [∗ ]Corresponding author. School of Information Management, Wuhan University, Wuhan, China
                Article
                Publisher Item ID: S0747-5632(22)00469-1 Publisher ID: 107649
                DOI: 10.1016/j.chb.2022.107649
                PMC ID: 9844095
                PubMed ID: 36683861
                SO-VID: 959dd689-8b88-4f9f-b75f-ce94545edb98
                Copyright © © 2022 Published by Elsevier Ltd.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 28 August 2022
                Date revision received : 25 December 2022
                Date accepted : 31 December 2022
                Categories
                Subject: Article

                Keywords: covid-19 pandemic,vaccine hesitancy,user polarization,curiosity-drive theory,big five model of personality
                Data availability:
                Keywords: covid-19 pandemic, vaccine hesitancy, user polarization, curiosity-drive theory, big five model of personality

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