{"title":"Examining racial stereotypes in YouTube autocomplete suggestions","authors":"Eunbin Ha, Haein Kong, Shagun Jhaver","doi":"10.1177/14614448251346503","DOIUrl":null,"url":null,"abstract":"Autocomplete is a popular search feature that predicts queries based on user input and guides users to a set of potentially relevant suggestions. In this study, we examine what YouTube autocompletes suggest to users seeking information about race on the platform. Specifically, we perform an algorithm output audit of autocomplete suggestions for input queries about four racial groups and examine the stereotypes they embody. Using critical discourse analysis, we identify five major sociocultural contexts in which racial information appears – <jats:italic>Appearance</jats:italic> , <jats:italic>Ability</jats:italic> , <jats:italic>Culture</jats:italic> , <jats:italic>Social Equity</jats:italic> , and <jats:italic>Manner</jats:italic> . We found that the participatory nature of YouTube produces a multifaceted representation of race-related content in its search outputs, characterized by enduring historical biases, aggregated discrimination, and interracial tensions, while simultaneously depicting minority resistance and aspirations of a post-racial society. We call for innovations in content moderation policy design and enforcement to address existing racial harms in YouTube search outputs.","PeriodicalId":19149,"journal":{"name":"New Media & Society","volume":"8 1","pages":""},"PeriodicalIF":4.5000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"New Media & Society","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1177/14614448251346503","RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMMUNICATION","Score":null,"Total":0}
引用次数: 0
Abstract
Autocomplete is a popular search feature that predicts queries based on user input and guides users to a set of potentially relevant suggestions. In this study, we examine what YouTube autocompletes suggest to users seeking information about race on the platform. Specifically, we perform an algorithm output audit of autocomplete suggestions for input queries about four racial groups and examine the stereotypes they embody. Using critical discourse analysis, we identify five major sociocultural contexts in which racial information appears – Appearance , Ability , Culture , Social Equity , and Manner . We found that the participatory nature of YouTube produces a multifaceted representation of race-related content in its search outputs, characterized by enduring historical biases, aggregated discrimination, and interracial tensions, while simultaneously depicting minority resistance and aspirations of a post-racial society. We call for innovations in content moderation policy design and enforcement to address existing racial harms in YouTube search outputs.
期刊介绍:
New Media & Society engages in critical discussions of the key issues arising from the scale and speed of new media development, drawing on a wide range of disciplinary perspectives and on both theoretical and empirical research. The journal includes contributions on: -the individual and the social, the cultural and the political dimensions of new media -the global and local dimensions of the relationship between media and social change -contemporary as well as historical developments -the implications and impacts of, as well as the determinants and obstacles to, media change the relationship between theory, policy and practice.