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Visual Bias

Giulia Caprini

No 1016, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: I study the non-verbal language of leading pictures in online news and its influence on readers’ opinions. I develop a visual vocabulary and use a dictionary approach to analyze around 300,000 photos published in US news in 2020. I document that the visual language of US media is politically partisan and significantly polarised. I then demonstrate experimentally that the news’ partisan visual language is not merely distinctive of outlets’ ideological positions, but also promotes them among readers. In a survey experiment, identical articles with images of opposing partisanships induce different opinions, tilted towards the pictures’ ideological poles. Moreover, as readers react more to images aligned with their viewpoint, the news’ visual bias causes issue polarization to increase. Finally, I find that media can effectively slant readers using neutral texts and partisan pictures: this result calls for the inclusion of image scrutiny in news assessments and fact checking, today largely text-based.

Date: 2023-05-03
New Economics Papers: this item is included in nep-big, nep-cul and nep-exp
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