{"title":"Rate and review: Exploring listener motivations for engagement with music podcasts","authors":"C. Hamilton, Simon Barber","doi":"10.1386/rjao_00053_1","DOIUrl":null,"url":null,"abstract":"Podcasts have become an important part of music reception practices, providing new ways of engaging with reviews and recommendations, artist interviews and popular music histories. This article presents a replicable working methodology that can be applied to study the data associated\n with podcasts of any genre. In our analysis, we explore approximately 16,000 listener reviews of the Top 50 podcasts in the Apple (UK) music chart in order to discover what it is about music podcasts that draws listeners to regularly engage with their favourite shows. This method, based on\n unsupervised machine learning algorithms, automates data-scraping for podcast reviews and ratings. We describe and critically reflect on this process in order to understand not only how listeners describe their range of motivations for engagement with music podcasts, but also the limitations\n of this approach in a media and cultural studies context.","PeriodicalId":38660,"journal":{"name":"Radio Journal","volume":"40 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radio Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1386/rjao_00053_1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Arts and Humanities","Score":null,"Total":0}
引用次数: 1
Abstract
Podcasts have become an important part of music reception practices, providing new ways of engaging with reviews and recommendations, artist interviews and popular music histories. This article presents a replicable working methodology that can be applied to study the data associated
with podcasts of any genre. In our analysis, we explore approximately 16,000 listener reviews of the Top 50 podcasts in the Apple (UK) music chart in order to discover what it is about music podcasts that draws listeners to regularly engage with their favourite shows. This method, based on
unsupervised machine learning algorithms, automates data-scraping for podcast reviews and ratings. We describe and critically reflect on this process in order to understand not only how listeners describe their range of motivations for engagement with music podcasts, but also the limitations
of this approach in a media and cultural studies context.
Radio JournalArts and Humanities-Visual Arts and Performing Arts
CiteScore
1.50
自引率
0.00%
发文量
7
期刊介绍:
Radio Journal publishes critical analyses of radio and sound media across a variety of platforms, from broadcast to podcast and all in between. Articles focus on both historical and contemporary issues in sound-based journalism and media studies. We look for work that explores the production, circulation and reception of radio and creative soundwork, and encourage a wide range of international and interdisciplinary perspectives. Radio Journal welcomes scholarship from early career researchers as well as internationally renowned scholars. It also publishes reviews of recent publications in the field of radio and sound studies. Radio Journal is edited from the US and Australia and has an international scope. It is a refereed publication; all research articles undergo rigorous double-blind peer review. The editors will review other contributions. The process normally takes three months to complete.