Visual Bias in the Brexit Referendum: A Quantitative Analysis of Newspaper Images

Wanyu Chung, Duiyi Dai and Robert Elliott

No 20524, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: In this paper, we investigate whether, and to what extent, UK newspapers exhibited image-based bias in their portrayal of politicians during the 2016 Brexit referendum, potentially shaping public perceptions. We use computer vision and machine learning techniques to first, identify the faces of politicians and assess the emotional content conveyed through their expressions and second, to measure the contextual sentiment of the overall image, including elements such as background, objects, and color. Our findings reveal that tabloid newspapers displayed significant partisan bias. Specifically, pro-leave tabloids were more likely to depict pro-leave politicians with positive facial expressions and in more favorable visual contexts, while pro-remain politicians were portrayed more negatively. This bias was especially pronounced in front-page images and those featuring key political figures, while no comparable patterns were found in broadsheets. However, the visual bias diminished immediately after the referendum vote. Our scalable framework offers a systematic way to detect visual bias in political imagery, with broader applicability to media coverage of other electoral or policy events.

Keywords: Visual media bias; image analysis; Machine learning; Brexit referendum (search for similar items in EconPapers)
JEL-codes: D83 D91 L82 (search for similar items in EconPapers)
Date: 2025-08
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