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Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis∗ Ceren Budak Microsoft Research
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Document Date: 2015-01-20 21:10:59


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Company

the Associated Press / the New York Times / Outlet BBC News Chicago Tribune / Amazon / Wall Street Journal / Huffington Post / Chicago Tribune / The Washington Post / USA Today Chicago Tribune / Patterson / USA Today / News Fox News Huffington Post Los Angeles Times / NBC / BBC News / New York Times Daily / Los Angeles Times / Yahoo / Microsoft / Reuters / CNN / /

Country

United States / /

/

IndustryTerm

Internet Explorer web browser / news media / partisan media bias / media bias / partisan media establishment / partisan media / web browsing / online crowd workers / supervised learning algorithms / finance / healthcare / online news sites / /

MarketIndex

set 340 / /

Organization

Republican Party / Democratic Party / Supreme Court / Stanford University / /

Person

Matthew Salganik / Sharad Goel / Seth Flaxman / Mason / Justin M. Rao / Sid Suri / /

PoliticalEvent

primary / /

Position

major U.S. news outlets / representative / single worker / corresponding author / /

PublishedMedium

the Wall Street Journal / the New York Times / the Chicago Tribune / Los Angeles Times / USA Today / Wall Street Journal / /

Technology

supervised learning algorithms / machine learning / /

URL

http /

SocialTag