{"title":"AGECovP: identifying ageism and analyzing COVID-19 discourse on older adults in YouTube.","authors":"Ghenai Amira, Nath Keshav, Satsangi Aarat","doi":"10.1140/epjds/s13688-025-00582-6","DOIUrl":null,"url":null,"abstract":"<p><p>The COVID-19 pandemic significantly impacted older adults, generating widespread online discussions that revealed how this at-risk population was perceived. Understanding these portrayals is essential, as public discourse influences societal perceptions of aging and impacts policies and practices affecting older adults. Past research highlights that ageist stereotypes and attitudes frequently surface in public discussions, shaping the experiences of older individuals. The current study presents AGECovP, a comprehensive dataset featuring a diverse collection of YouTube videos, a leading social media platform. AGECovP is designed to provide researchers with meaningful insights into how older adults were portrayed during the pandemic and how topics such as conspiracy theories, misinformation, and the anti-vaccine movement were framed in relation to aging populations. In addition, the dataset includes a set of labeled comments indicating the presence of ageist content, enabling researchers to perform ageist detection and analyze ageism in online discourse. By providing a resource for examining both overt and subtle forms of ageism, AGECovP contributes to the development of tools and methodologies for addressing bias against older adults. This dataset fosters actionable insights into societal attitudes, enhancing the development of inclusive policies and interventions. Our data is available at: https://zenodo.org/records/15800324.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"14 1","pages":"65"},"PeriodicalIF":2.5000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12390874/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EPJ Data Science","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1140/epjds/s13688-025-00582-6","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/8/27 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The COVID-19 pandemic significantly impacted older adults, generating widespread online discussions that revealed how this at-risk population was perceived. Understanding these portrayals is essential, as public discourse influences societal perceptions of aging and impacts policies and practices affecting older adults. Past research highlights that ageist stereotypes and attitudes frequently surface in public discussions, shaping the experiences of older individuals. The current study presents AGECovP, a comprehensive dataset featuring a diverse collection of YouTube videos, a leading social media platform. AGECovP is designed to provide researchers with meaningful insights into how older adults were portrayed during the pandemic and how topics such as conspiracy theories, misinformation, and the anti-vaccine movement were framed in relation to aging populations. In addition, the dataset includes a set of labeled comments indicating the presence of ageist content, enabling researchers to perform ageist detection and analyze ageism in online discourse. By providing a resource for examining both overt and subtle forms of ageism, AGECovP contributes to the development of tools and methodologies for addressing bias against older adults. This dataset fosters actionable insights into societal attitudes, enhancing the development of inclusive policies and interventions. Our data is available at: https://zenodo.org/records/15800324.
期刊介绍:
EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.