A. D. de Rosa, Emanuele Fino, A. Holman, Bishoy Hanna-Khalil
{"title":"Social representations of COVID-19 vaccines: Exploration of user-generated comments via online video sharing during the first year of the pandemic","authors":"A. D. de Rosa, Emanuele Fino, A. Holman, Bishoy Hanna-Khalil","doi":"10.1177/18344909221147648","DOIUrl":null,"url":null,"abstract":"The current study aimed to explore the public understanding of COVID-19 vaccines and the social representations emerging from a corpus of user-generated comments on YouTube videos posted during the year following the World Health Organization's declaration of the novel coronavirus as pandemic. We used Structural Topic Modelling to process the text and identified a 10-topic solution as the best to represent the corpus of text data. The exploration of the topics showed a complex landscape of social representations underlying a plurality of perspectives, which we interpreted as reflecting different users’ needs to make sense of the unprecedented events. Implications for theory, future research, and intervention for health psychology and policy are discussed.","PeriodicalId":45049,"journal":{"name":"Journal of Pacific Rim Psychology","volume":" ","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Pacific Rim Psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/18344909221147648","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1
Abstract
The current study aimed to explore the public understanding of COVID-19 vaccines and the social representations emerging from a corpus of user-generated comments on YouTube videos posted during the year following the World Health Organization's declaration of the novel coronavirus as pandemic. We used Structural Topic Modelling to process the text and identified a 10-topic solution as the best to represent the corpus of text data. The exploration of the topics showed a complex landscape of social representations underlying a plurality of perspectives, which we interpreted as reflecting different users’ needs to make sense of the unprecedented events. Implications for theory, future research, and intervention for health psychology and policy are discussed.