{"title":"媒体数据与疫苗犹豫:范围界定综述(预印本)","authors":"J. Yin","doi":"10.2196/preprints.37300","DOIUrl":null,"url":null,"abstract":"\n BACKGROUND\n Media studies are important for vaccine hesitancy research since they analyze how media shapes risk perceptions and uptake of vaccines. Despite a growth in studies in this field due to advances in computing, language processing, and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology.\n \n \n OBJECTIVE\n The review aimed to identify and illustrate what media platforms and methods were used to study vaccine hesitancy, and how they build or contribute to the study of media’s influence on vaccine hesitancy and public health.\n \n \n METHODS\n This study followed PRISMA’s (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines for scoping reviews. A search was done of PubMed, Web of Science, and SCOPUS for any studies that: used media data (social media and/or traditional media); had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, stance); were written in English; and published after 2010. Studies were screened by only one reviewer, and extracted for media platform, analysis method, and theoretical models used.\n \n \n RESULTS\n A total of 123 studies were included, 69 which used traditional research methods and 54 which used computational methods. Of the traditional methods, a majority used content analysis (74.2%) and sentiment analysis (37.1%) to analyze texts, with few using campaign evaluation methods (8.1%) and tracking of search activity and/or information spread (11.3%). The most common platform was newspapers, print media, and online news. Computational methods mostly used sentiment analysis (57.4%), topic modelling (31.5%), and network analysis (27.8%). Fewer studies used projections and feature extraction as methods. The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. Of traditional methods, only 8 had a theory-based approach (11.6%); and for computational methods, only 6 (11.1%). Due to a patchwork of studies resulting from combinations of platforms and methods, it is difficult to draw a coherent conclusion on media’s influence on vaccine hesitancy.\n \n \n CONCLUSIONS\n There is heterogeneity in using media data to study vaccine hesitation, evidenced in the medley of combinations of platforms and computer science tools (eg. network analysis, sentiment analysis). Yet, these studies are guided by a preference for using novel methods rather than theory, making their links to public health tenuous. This review suggests and walks through a theory-first approach that can aid in better formulation of knowledge and establish a coherent paradigm in media studies on vaccine hesitancy. It ends with a statement that media data analyses, though groundbreaking in approach, should supplement–not supplant–current practices in public health research.\n","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":" ","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Media data and vaccine hesitancy: a scoping review (Preprint)\",\"authors\":\"J. Yin\",\"doi\":\"10.2196/preprints.37300\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n BACKGROUND\\n Media studies are important for vaccine hesitancy research since they analyze how media shapes risk perceptions and uptake of vaccines. Despite a growth in studies in this field due to advances in computing, language processing, and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology.\\n \\n \\n OBJECTIVE\\n The review aimed to identify and illustrate what media platforms and methods were used to study vaccine hesitancy, and how they build or contribute to the study of media’s influence on vaccine hesitancy and public health.\\n \\n \\n METHODS\\n This study followed PRISMA’s (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines for scoping reviews. A search was done of PubMed, Web of Science, and SCOPUS for any studies that: used media data (social media and/or traditional media); had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, stance); were written in English; and published after 2010. Studies were screened by only one reviewer, and extracted for media platform, analysis method, and theoretical models used.\\n \\n \\n RESULTS\\n A total of 123 studies were included, 69 which used traditional research methods and 54 which used computational methods. Of the traditional methods, a majority used content analysis (74.2%) and sentiment analysis (37.1%) to analyze texts, with few using campaign evaluation methods (8.1%) and tracking of search activity and/or information spread (11.3%). The most common platform was newspapers, print media, and online news. Computational methods mostly used sentiment analysis (57.4%), topic modelling (31.5%), and network analysis (27.8%). Fewer studies used projections and feature extraction as methods. The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. Of traditional methods, only 8 had a theory-based approach (11.6%); and for computational methods, only 6 (11.1%). Due to a patchwork of studies resulting from combinations of platforms and methods, it is difficult to draw a coherent conclusion on media’s influence on vaccine hesitancy.\\n \\n \\n CONCLUSIONS\\n There is heterogeneity in using media data to study vaccine hesitation, evidenced in the medley of combinations of platforms and computer science tools (eg. network analysis, sentiment analysis). Yet, these studies are guided by a preference for using novel methods rather than theory, making their links to public health tenuous. This review suggests and walks through a theory-first approach that can aid in better formulation of knowledge and establish a coherent paradigm in media studies on vaccine hesitancy. It ends with a statement that media data analyses, though groundbreaking in approach, should supplement–not supplant–current practices in public health research.\\n\",\"PeriodicalId\":73554,\"journal\":{\"name\":\"JMIR infodemiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2022-02-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR infodemiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2196/preprints.37300\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR infodemiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/preprints.37300","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
摘要
背景媒体研究对疫苗犹豫研究很重要,因为它们分析了媒体如何塑造风险认知和疫苗接种。尽管由于计算、语言处理和社交媒体领域的发展,该领域的研究有所增长,但没有任何研究巩固了用于研究疫苗犹豫的方法论方法。综合这些信息可以更好地构建数字流行病学这一不断发展的子领域,并为其开创先例。目的本综述旨在确定和说明哪些媒体平台和方法用于研究疫苗犹豫,以及它们如何建立或促进研究媒体对疫苗犹豫和公共卫生的影响。方法本研究遵循PRISMA(系统评价和荟萃分析的首选报告项目)的范围界定审查指南。在PubMed、Web of Science和SCOPUS上搜索任何研究:使用媒体数据(社交媒体和/或传统媒体);结果与疫苗情绪有关(意见、接受、犹豫、接受、立场);用英语书写;2010年后出版。研究仅由一名评审员进行筛选,并提取所使用的媒体平台、分析方法和理论模型。结果共纳入123项研究,其中69项采用传统研究方法,54项采用计算方法。在传统方法中,大多数使用内容分析(74.2%)和情绪分析(37.1%)来分析文本,很少使用活动评估方法(8.1%)和搜索活动和/或信息传播跟踪(11.3%)。最常见的平台是报纸、平面媒体和在线新闻。计算方法主要使用情感分析(57.4%)、主题建模(31.5%)和网络分析(27.8%)。使用投影和特征提取作为方法的研究较少。最常见的平台是Twitter和Facebook。理论上,大多数研究都很薄弱。在传统方法中,只有8种方法采用基于理论的方法(11.6%);在计算方法方面,只有6个(11.1%)。由于平台和方法的组合导致了拼凑的研究,很难就媒体对疫苗犹豫的影响得出一致的结论。结论使用媒体数据研究疫苗犹豫存在异质性,平台和计算机科学工具(如网络分析、情绪分析)的组合证明了这一点。然而,这些研究倾向于使用新颖的方法而不是理论,这使得它们与公共卫生的联系变得脆弱。这篇综述提出并实践了一种理论至上的方法,这种方法可以帮助更好地制定知识,并在媒体对疫苗犹豫的研究中建立一个连贯的范式。文章最后指出,媒体数据分析虽然在方法上具有开创性,但应补充而不是取代公共卫生研究中的现行做法。
Media data and vaccine hesitancy: a scoping review (Preprint)
BACKGROUND
Media studies are important for vaccine hesitancy research since they analyze how media shapes risk perceptions and uptake of vaccines. Despite a growth in studies in this field due to advances in computing, language processing, and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology.
OBJECTIVE
The review aimed to identify and illustrate what media platforms and methods were used to study vaccine hesitancy, and how they build or contribute to the study of media’s influence on vaccine hesitancy and public health.
METHODS
This study followed PRISMA’s (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines for scoping reviews. A search was done of PubMed, Web of Science, and SCOPUS for any studies that: used media data (social media and/or traditional media); had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, stance); were written in English; and published after 2010. Studies were screened by only one reviewer, and extracted for media platform, analysis method, and theoretical models used.
RESULTS
A total of 123 studies were included, 69 which used traditional research methods and 54 which used computational methods. Of the traditional methods, a majority used content analysis (74.2%) and sentiment analysis (37.1%) to analyze texts, with few using campaign evaluation methods (8.1%) and tracking of search activity and/or information spread (11.3%). The most common platform was newspapers, print media, and online news. Computational methods mostly used sentiment analysis (57.4%), topic modelling (31.5%), and network analysis (27.8%). Fewer studies used projections and feature extraction as methods. The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. Of traditional methods, only 8 had a theory-based approach (11.6%); and for computational methods, only 6 (11.1%). Due to a patchwork of studies resulting from combinations of platforms and methods, it is difficult to draw a coherent conclusion on media’s influence on vaccine hesitancy.
CONCLUSIONS
There is heterogeneity in using media data to study vaccine hesitation, evidenced in the medley of combinations of platforms and computer science tools (eg. network analysis, sentiment analysis). Yet, these studies are guided by a preference for using novel methods rather than theory, making their links to public health tenuous. This review suggests and walks through a theory-first approach that can aid in better formulation of knowledge and establish a coherent paradigm in media studies on vaccine hesitancy. It ends with a statement that media data analyses, though groundbreaking in approach, should supplement–not supplant–current practices in public health research.