Rate and review: Exploring listener motivations for engagement with music podcasts.
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 o...
| Published in: | Radio Journal: International Studies in Broadcast & Audio Media Vol. 20; no. 1; pp. 17 - 33 |
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| Main Authors: | , |
| Format: | Article |
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Intellect Ltd.
Apr2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=158057396&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 158057396 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 14764504 L2N jtl: Radio Journal: International Studies in Broadcast & Audio Media issn: 14764504 maglogo: N pubinfo: dt: Apr2022 vid: 20 iid: 1 pid: 12270 pub: Intellect Ltd. artinfo: ui: 158057396 10.1386/rjao_00053_1 ppf: 17 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 425KB tig: atl: Rate and review: Exploring listener motivations for engagement with music podcasts. aug: au: Hamilton, Craig Barber, Simon affil: Birmingham City University su: United Kingdom Popular music Podcasting Music charts Machine learning Practicing (Music performance) sug: subj: Popular music United Kingdom Internet Publishing and Broadcasting and Web Search Portals Podcasting Music charts Machine learning Practicing (Music performance) keyword: data digital humanities machine learning natural language processing podcasting reception data digital humanities machine learning natural language processing podcasting reception ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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