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UP NEXT: YouTube's Recommendation System and the 2019 Canadian Federal Election
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- Author / Creator
- Cockcroft, Daniel
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In the months leading up to the 2016 election in the United States, YouTube’s recommendation algorithm decidedly favored pro-Trump videos, fake news and conspiracy theories. In this thesis, I question whether such bias is present in the context of the 2019 federal election in Canada. To do so, I make use of open-source software to gather recommendation data related to three of the candidates: Conservative Party of Canada leader Andrew Scheer, New Democratic Party leader Jagmeet Singh, and Liberal Party of Canada leader Justin Trudeau. Using the same data, I will also study the media bias and factual accuracy of the sources recommended. My results show that YouTube’s recommender system is susceptible to influence by audiences and shows bias towards Andrew Scheer and against Justin Trudeau. Given my results and evidence provided by other researchers, this study stresses the need for ethical algorithm design, including proactive approaches for increased transparency, regulatory oversight, and increased public awareness.
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- Subjects / Keywords
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- Graduation date
- Fall 2020
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- Type of Item
- Thesis
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- Degree
- Master of Arts/Master of Library and Information Studies
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- License
- Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.