Article | Open Access
Echoes of Emotion: Influencers’ Communication Strategies and Comment Polarization in the US 2024 Presidential Election
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Abstract: This study examines strategic differences between Democratic and Republican YouTube influencers during the 2024 US presidential election and their association with comment-level political polarization. Drawing on digital opinion leadership theory, platform architecture perspectives, and visual framing research, we employ a mixed-methods design integrating framing analysis with latent Dirichlet allocation topic modeling. The study analyzes 373 videos and 371,124 user comments published between July 27 and November 30, 2024. These data were sourced from 40 YouTube channels with at least 100,000 subscribers. Findings reveal that Democratic creators adopted a moral-crisis framing strategy while Republicans emphasized an opportunity–explanation framing model, and that 56.4% of comment discourse centers on candidate personality rather than policy substance, exhibiting patterns consistent with affective polarization. The study develops and empirically calibrates the YouTube polarization risk assessment scale through ordinary least squares regression analysis (R² = 0.224, p < 0.001), identifying political stance strength and humor/sarcasm as the strongest predictors of comment polarization, thereby providing an exploratory, empirically informed framework for assessing polarization risk in platform-based political communication.
Keywords: affective polarization; electoral communication; polarization; political communication; political influencers; YouTube
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Vol 14 (2026): Communication in Election Campaigns: Staggering Changes or Same Old, Same Old? (In Progress)
© Xianquan Zeng, Jiarun Du, Jinghong Xu, Qiong Xu. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0), which permits any use, distribution, and reproduction of the work without further permission provided the original author(s) and source are credited.