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Quantifying Bias of the U.S. Media: ...

Williams, Emily.

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Quantifying Bias of the U.S. Media: A Spatial Analysis of Product Differentiation on Social Media.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantifying Bias of the U.S. Media: A Spatial Analysis of Product Differentiation on Social Media./
作者:	Williams, Emily.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	120 p.
附註:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
標題:	Economics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10690517
ISBN:	9780355795127

Quantifying Bias of the U.S. Media: A Spatial Analysis of Product Differentiation on Social Media.
Williams, Emily.

Quantifying Bias of the U.S. Media: A Spatial Analysis of Product Differentiation on Social Media. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 120 p.

Source: Masters Abstracts International, Volume: 57-05.

Thesis (M.S.)--The University of North Carolina at Charlotte, 2017.

In this study, I create a measure of ideological bias as a means for U.S. news sources to differentiate their products in the growing digital space. In addition to traditional media platforms, such as newspapers and television broadcasts, I include a number of digital-born source established over the last several decades. I compute average sentiments of tweets by U.S. Congress members and news outlets that mention Hillary Clinton, Donald Trump, the Republican party, or the Democratic party. Employing maximum likelihood estimation of a logistic regression, I determine the log-likelihood a source would be classified as liberal, which I use as a proxy for ideological score.

ISBN: 9780355795127Subjects--Topical Terms:

517137
Economics.

Quantifying Bias of the U.S. Media: A Spatial Analysis of Product Differentiation on Social Media.
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520 $a In this study, I create a measure of ideological bias as a means for U.S. news sources to differentiate their products in the growing digital space. In addition to traditional media platforms, such as newspapers and television broadcasts, I include a number of digital-born source established over the last several decades. I compute average sentiments of tweets by U.S. Congress members and news outlets that mention Hillary Clinton, Donald Trump, the Republican party, or the Democratic party. Employing maximum likelihood estimation of a logistic regression, I determine the log-likelihood a source would be classified as liberal, which I use as a proxy for ideological score.
520 $a I did find evidence of ideological differences due to the large span of scores. Consistent with public perception, I found The Grio, the New York Times, and Mother Jones to be the most liberal papers with scores close to the average Democrat and Bernie Sanders. Similarly, Townhall.com and Right Side Broadcasting Network were considered most conservative, but fell well above the average Republican score.
520 $a There was some evidence of newer organizations positioning themselves further from center. For instance, Townhall.com and Right Side Broadcasting Network had scores below 30%, whereas The Grio and NewsOne scored above 80%. However, the majority of news sources earned scores between 40% and 65%, but 27 out of the 40 examined were ranked above 50% implying the overall media tends to bias more to the left. The logit model results also did not yield definitive clusters among newer digitally-founded organizations. Additionally, the results of the model utilized were not robust.
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