Over the past decade, U.S. electioneering has undergone a fundamental transformation, shifting from broadcast-centric persuasion to an algorithmically mediated competition for attention. Social media platforms such as X (formerly Twitter), Facebook, Instagram, and TikTok now function as central infrastructures for political communication, shaping information flows, amplification dynamics, and audience segmentation. This paper develops a conceptual framework of algorithmic campaign dynamics to explain how perceived algorithmic influence affects political communication and engagement in contemporary U.S. elections. Drawing on classical media effects theories, agenda-setting and framing and integrating recent scholarship on algorithmic amplification, selective exposure, and echo chambers, the framework traces a causal chain linking perceived algorithmic influence to misinformation exposure, echo chamber formation, affective polarization, and political engagement. The study contributes to the literature by (1) bridging traditional media theories with algorithmic mediation, (2) integrating psychological and behavioral outcomes within a single model, and (3) highlighting platform-specific differences in political communication dynamics. A proposed quantitative survey design is outlined to guide future empirical testing, with potential for complementary experimental and computational approaches. By offering a theoretically grounded roadmap, this paper provides scholars, practitioners, and policymakers with a structured lens for understanding how algorithmic environments shape electoral behavior and democratic resilience.
Keywords: Social media, U.S. elections, affective polarization, algorithmic campaign dynamics, echo chambers, misinformation, political engagement, selective exposure
