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Towards an Institutional News Logic of Digital Native News Media? A Case Study of BuzzFeed’s Reporting During the 2015 and 2017 UK General Election Campaigns

Richard Thomas Orcid Logo, Stephen Cushion

Digital Journalism, Pages: 1 - 18

Swansea University Author: Richard Thomas Orcid Logo

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DOI (Published version): 10.1080/21670811.2019.1661262

Abstract

Informed by new institutional perspectives to debates about theorising media logic, this study asks whether a popular digital native media platform has, over time, conformed to a singular news logic associated with the norms and routines of legacy media. Drawing on a content analysis of 399 BuzzFeed...

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Published in: Digital Journalism
ISSN: 2167-0811 2167-082X
Published: 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa51892
first_indexed 2019-09-13T14:30:44Z
last_indexed 2020-11-24T04:05:16Z
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spelling 2020-11-23T14:47:56.2186132 v2 51892 2019-09-13 Towards an Institutional News Logic of Digital Native News Media? A Case Study of BuzzFeed’s Reporting During the 2015 and 2017 UK General Election Campaigns 6458b4d9c68a8d6431e86961e74dccb5 0000-0003-3511-5628 Richard Thomas Richard Thomas true false 2019-09-13 CACS Informed by new institutional perspectives to debates about theorising media logic, this study asks whether a popular digital native media platform has, over time, conformed to a singular news logic associated with the norms and routines of legacy media. Drawing on a content analysis of 399 BuzzFeed news items and 1878 sources during the 2015 and 2017 UK general election campaigns, we established that coverage had shifted, reflecting an editorial agenda that is consistent with how legacy media have long reported politics. In the 2017 election campaign there was more substantive policy reported, new specialist reporters employed, a greater reliance on institutional sources, particularly from established legacy media, and a sharper focus on the two main political parties. Overall, we argue that, as digital native media have evolved, become more popular and interconnected with legacy media, the norms and routines of their news reporting are not necessarily that distinguishable from a singular, institutional news media logic. Journal Article Digital Journalism 1 18 2167-0811 2167-082X Digital native media, media logic, election reporting, journalism, content analysis, institutional news logic 12 9 2019 2019-09-12 10.1080/21670811.2019.1661262 COLLEGE NANME Culture and Communications School COLLEGE CODE CACS Swansea University 2020-11-23T14:47:56.2186132 2019-09-13T09:59:48.6395774 Faculty of Humanities and Social Sciences School of Culture and Communication - Media, Communications, Journalism and PR Richard Thomas 0000-0003-3511-5628 1 Stephen Cushion 2 0051892-23092019091956.pdf 51892.pdf 2019-09-23T09:19:56.7770000 Output 607036 application/pdf Accepted Manuscript true 2021-03-12T00:00:00.0000000 true eng
title Towards an Institutional News Logic of Digital Native News Media? A Case Study of BuzzFeed’s Reporting During the 2015 and 2017 UK General Election Campaigns
spellingShingle Towards an Institutional News Logic of Digital Native News Media? A Case Study of BuzzFeed’s Reporting During the 2015 and 2017 UK General Election Campaigns
Richard Thomas
title_short Towards an Institutional News Logic of Digital Native News Media? A Case Study of BuzzFeed’s Reporting During the 2015 and 2017 UK General Election Campaigns
title_full Towards an Institutional News Logic of Digital Native News Media? A Case Study of BuzzFeed’s Reporting During the 2015 and 2017 UK General Election Campaigns
title_fullStr Towards an Institutional News Logic of Digital Native News Media? A Case Study of BuzzFeed’s Reporting During the 2015 and 2017 UK General Election Campaigns
title_full_unstemmed Towards an Institutional News Logic of Digital Native News Media? A Case Study of BuzzFeed’s Reporting During the 2015 and 2017 UK General Election Campaigns
title_sort Towards an Institutional News Logic of Digital Native News Media? A Case Study of BuzzFeed’s Reporting During the 2015 and 2017 UK General Election Campaigns
author_id_str_mv 6458b4d9c68a8d6431e86961e74dccb5
author_id_fullname_str_mv 6458b4d9c68a8d6431e86961e74dccb5_***_Richard Thomas
author Richard Thomas
author2 Richard Thomas
Stephen Cushion
format Journal article
container_title Digital Journalism
container_start_page 1
publishDate 2019
institution Swansea University
issn 2167-0811
2167-082X
doi_str_mv 10.1080/21670811.2019.1661262
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Culture and Communication - Media, Communications, Journalism and PR{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Culture and Communication - Media, Communications, Journalism and PR
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description Informed by new institutional perspectives to debates about theorising media logic, this study asks whether a popular digital native media platform has, over time, conformed to a singular news logic associated with the norms and routines of legacy media. Drawing on a content analysis of 399 BuzzFeed news items and 1878 sources during the 2015 and 2017 UK general election campaigns, we established that coverage had shifted, reflecting an editorial agenda that is consistent with how legacy media have long reported politics. In the 2017 election campaign there was more substantive policy reported, new specialist reporters employed, a greater reliance on institutional sources, particularly from established legacy media, and a sharper focus on the two main political parties. Overall, we argue that, as digital native media have evolved, become more popular and interconnected with legacy media, the norms and routines of their news reporting are not necessarily that distinguishable from a singular, institutional news media logic.
published_date 2019-09-12T13:49:14Z
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score 11.048626

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