Algorithmic amplification of geopolitical narratives and user engagement in the 2024 U. S. presidential election

Abstract

This study examines how geopolitical narratives were amplified through presidential campaign communication on YouTube during the 2024 U. S. presidential election, and the extent to which such narratives were associated with differential user engagement (operationalized as a platform-based engagement proxy) compared to content related to domestic issues. Drawing on agenda-setting theory and framing theory as a dual theoretical lens, the study employed computational content analysis of 5,466 videos collected through the YouTube Data API v3 from official campaign channels and affiliated political organizations spanning January 2024 through Election Day (5 November 2024). Topic classification was performed using GPT-4 and BERT-based models, and sentiment analysis was conducted using RoBERTa and GPT-assisted coding. All AI-generated topic and frame classifications were independently verified against trained human coders, with inter-coder reliability meeting accepted thresholds (Krippendorff’s α  = 0.78–0.86; Cohen’s κ  = 0.76–0.84). Three statistical models were applied: a negative binomial regression, a one-way analysis of variance (ANOVA), and a multiple regression. Results show that videos featuring geopolitical narratives generated significantly higher mean engagement ( M  = 88,004; SD = 74,122) than videos on domestic issues ( M  = 62,445; SD = 48,112), with immigration and border security content generating the highest overall engagement ( M  = 118,647). Among framing strategies, threat framing emerged as the most powerful predictor of engagement ( β  = 0.34, p  < 0.001), followed by national security framing ( β  = 0.24, p  < 0.001) and patriotism framing ( β  = 0.21, p  < 0.001). Negative sentiment scores also contributed significantly to engagement outcomes ( β  = 0.29, p  < 0.001), suggesting that emotionally charged, threat-oriented geopolitical content is associated with the engagement signals that platform recommendation systems are designed to reward. These findings carry implications for democratic communication, platform governance, and the strategic use of AI-assisted political messaging in contemporary electoral campaigns.