Analyze the Role of Generative Artificial Intelligence in Shaping Public Opinion and Democratic Processes

Abstract

Generative artificial intelligence has rapidly emerged as a transformative technology capable of producing highly realistic text, images, audio, and video content. Advanced generative models such as large language models and diffusion-based image generators are increasingly integrated into digital platforms including social media, news generation systems, and automated communication tools. While these technologies offer opportunities for innovation, creativity, and information accessibility, they also raise significant concerns regarding their influence on public opinion and democratic processes. In contemporary digital societies, public discourse and political participation are heavily shaped by online information environments. Generative artificial intelligence systems can produce persuasive political narratives, automated commentary, and synthetic media that may influence citizens’ perceptions of political issues, candidates, and public policies. The growing ability of generative artificial intelligence to produce large volumes of realistic content introduces potential risks related to misinformation, manipulation of public discourse, and erosion of trust in democratic institutions. Automated generation of political messages, deepfake videos, and targeted information campaigns may alter the dynamics of political communication and electoral processes. Consequently, understanding the impact of generative artificial intelligence on democratic governance has become an important area of interdisciplinary research. This study analyzes the role of generative artificial intelligence in shaping public opinion and democratic processes. The research develops a conceptual model that examines the relationships between generative artificial intelligence content exposure, perceived information credibility, misinformation risk, and democratic engagement. Data were collected from digital media users, political communication experts, and information technology professionals. Structural Equation Modeling using Smart Partial Least Squares was applied to analyze the relationships between constructs. The results indicate that exposure to generative artificial intelligence generated content significantly influences perceptions of information credibility and increases the risk of misinformation within digital communication environments. However, media literacy and regulatory governance mechanisms play important roles in mitigating the negative effects of automated content generation. The study contributes to research on digital democracy and artificial intelligence governance by providing empirical insights into how generative artificial intelligence technologies shape political communication and democratic participation in the digital age