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Abstract
This article examines if patterns in online news seeking privilege stories featuring more linguistic markers of partisan affect than those positioned by traditional gatekeepers on the print front page. Online “most-read” and print front-page stories covering 8 weeks of the 2012 presidential campaign were submitted to computer-assisted text analysis (n = 302). Guided by research on online and partisan affect, this study hypothesizes that (a) “most-read” stories will feature more supportive language than stories placed on the front page by traditional gatekeepers when the news outlet has a reputation for supporting the incumbent party; and (b) “most-read” stories will feature more antagonistic language than those placed on the front page by traditional gatekeepers when the news outlet has a reputation for supporting the challenger party. The findings show how online audiences opted for stories that featured more linguistic markers of preferred partisan affect than journalists and editors placed on Page One.
Notes
[1] Visit dictionsoftware.com or contact the first author for more information about this methodology.
[2] Care was taken to guard against type I error by calculating a Bonferroni adjustment.