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Normalization and differentiation in Google News: a multi-method analysis of the world's largest news aggregator
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Title
Normalization and differentiation in Google News: a multi-method analysis of the world's largest news aggregator
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Wang
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Qun
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Qun Wang
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author
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Susan
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Susan Keith
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Advisory Committee
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chair
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Napoli
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Philip
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Philip Napoli
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Advisory Committee
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internal member
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Weber
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Matthew
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Matthew Weber
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Advisory Committee
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internal member
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Lewis
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Seth
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Seth Lewis
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Advisory Committee
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Rutgers University
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degree grantor
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School of Graduate Studies
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theses
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2020
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2020-01
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2020
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English
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This dissertation examines the history, evolution, and influence of Google News—Google’s news aggregation service—from the late 1990s to 2019. There are scattered studies about Google News, but no systematic and substantive research on what is, at this writing, its nearly two decades of existence. Those two decades happened to be a period of time that witnessed the intense digital transformation of the media industry and our society. Both the object of the study and the timeframe this study examines are significant to understand the changing media and technology environment in the 21st century. Drawing on normalization and differentiation theoretical frameworks, this dissertation combines traditional research methods and computational approaches to conduct historical research, web archival analysis, legal analysis, algorithm analysis, and more. In six chapters, this dissertation traces the origin and early history of Google News; the structural, visual, and functional evolution on the Google News homepage design since its launch in 2002; disputes about Google and its news aggregation service in different parts of the world; Google’s news-related technologies and algorithms; and Google’s systematic initiatives in the news area and their influences. Based on the analysis of the normalization and differentiation trends and the driving forces behind these trends, this study proposes an N-D-N theoretical model that conceptualizes Google’s development in the news area and the interaction between Google and the news industry. The dissertation concludes with a discussion about the implications of the N-D-N model for policymaking on platform governance and the future of journalism. Using Google News as a case study, this dissertation provides a snapshot of the changing media landscape in the digital era. It also makes theoretical and empirical contributions to the ongoing conversation about the interrelationship between digital platforms and the traditional media industry.
Subject
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Communication, Information and Library Studies
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News Web sites
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Rutgers University Electronic Theses and Dissertations
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ETD_10519
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1 online resource (iii, 256 pages) : illustrations
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Ph.D.
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Includes bibliographical references
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Google (Firm)
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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doi:10.7282/t3-cn0p-6g66
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ETD doctoral
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Rights
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The author owns the copyright to this work.
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Wang
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Qun
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Permission or license
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2020-01-08 20:23:22
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Qun Wang
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Rutgers University. School of Graduate Studies
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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2020-01-31
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2021-01-30
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Access to this PDF has been restricted at the author's request. It will be publicly available after January 30th, 2021.
Copyright
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Copyright protected
Availability
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Open
Reason
Permission or license
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2020-07-07T10:20:00
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