https://doi.org/10.1140/epjds/s13688-023-00404-7
Regular Article
Perceived masculinity from Facebook photographs of candidates predicts electoral success
1
School of AI Convergence, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, Republic of Korea
2
Department of Communication, University of California Los Angeles, 90095, Los Angeles, California, United States
a
kunwoo.park@ssu.ac.kr
b
jjoo@comm.ucla.edu
Received:
27
March
2023
Accepted:
6
July
2023
Published online:
23
August
2023
Politicians have used the web and social media to circumvent the gatekeeping behavior of traditional mass media by directly communicating with supporters in their accounts. This paper is aimed at understanding communication strategies used by politicians and campaigns, focusing on the role of gender cues in their visual self-presentation and their impact on election outcomes. Previous research has discussed the importance of visual portrayals of leaders in campaigns. These studies, however, have been mainly based on manual coding and are limited in scale and scope. This paper aims to fill the research gap by introducing a multi task method that infers perceived gender-stereotypical visual traits from social media images. We analyze 77,861 photographs collected from the Facebook accounts of 554 US politicians who ran in the 2018 elections. Regression analyses discover the positive association of the masculinity trait for electoral outcomes. We also identify an empirical evidence that the effect of gender stereotypes could vary according to the gender and party combinations of the candidates in a race. In the intersectional analysis, we found that the win of female democrats against the same gendered opponent was positively correlated with the femininity trait score. This study provides methodological foundations and empirical contributions to the understanding of politicians’ campaign behaviors via photographs shared on social media and their relation to electoral success.
Key words: Visual communication / Personal trait / Gender stereotype / Political campaign / Deep learning / Computer vision
© The Author(s) 2023
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
