Mechanisms of Platform Algorithm-Driven Content Diffusion in New Media: Evidence from Traffic Allocation Systems

Abstract

In the era of platformized communication, algorithm-driven recommendation systems have fundamentally reshaped content diffusion on new media platforms. Unlike traditional communication models shaped by editorial gatekeeping and follower-based networks, contemporary platforms such as TikTok and YouTube Shorts rely on traffic-driven algorithms to accelerate content diffusion and maximize user engagement. This study investigates how platform algorithms reshape content diffusion through traffic allocation mechanisms and explores the communication logic underlying algorithmic amplification. Drawing on recent studies in platformization, algorithmic governance, and information diffusion, this study adopts a qualitative research design combining literature review, comparative platform case analysis, and mechanism analysis. The study focuses on three research questions: how algorithmic recommendations accelerate content dissemination, how traffic mechanisms influence visibility and user interaction, and what communicative outcomes emerge from algorithm-driven diffusion. The findings indicate that personalized recommendations, real-time feedback systems, and algorithmic traffic allocation mechanisms significantly enhance content dissemination efficiency while simultaneously reinforcing the concentration of user attention and emotional amplification. The paper argues that platform algorithms have evolved from technical tools into key actors in the communication process, reshaping both the logic of content dissemination and the structure of digital public communication.