Slanted images: Measuring nonverbal media bias

Levi Boxell

MPRA Paper from University Library of Munich, Germany

Abstract: I build a dataset of over one million images used on the front page of websites around the 2016 election period. I then use machine-learning tools to detect the faces of politicians across the images and measure the nonverbal emotional content expressed by each politician. Combining this with data on the partisan composition of each website’s users, I show that websites portray politicians that align with the partisan preferences of their users with more positive emotions. I also find that nonverbal coverage by Republican-leaning websites was not consistent over the 2016 election, but became more favorable towards Donald Trump after he clinched the Republican nomination.

Keywords: media bias; images; emotions; nonverbal; polarization (search for similar items in EconPapers)
JEL-codes: C0 H0 L82 L86 (search for similar items in EconPapers)
Date: 2018-09-17
New Economics Papers: this item is included in nep-big, nep-ict and nep-pol
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:89047

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