The Impact of Low-Quality AI-Generated Content on Information Credibility in the Digital Sphere

Abstract

This article aims to examine the impact of low-quality AI-generated content, termed "AI Slop," on information credibility in the digital sphere, while analyzing its driving factors, dissemination mechanisms, and proposing systematic countermeasures. Using analysis and synthesis of relevant academic literature and scholarly documents, the study found that AI Slop progresses through four developmental stages: initial excitement, habituation, saturation and clutter, and cloying disgust. Drawing on Information Overload Theory, Signal-to-Noise Ratio concepts, and Media Ecology Theory, the study explains how low-quality content erodes the information ecosystem. Key characteristics of low-quality AI content include repetitiveness, confidently presented misinformation (hallucinations), lack of deep logical coherence, and mass production at near-zero cost. These characteristics generate impacts across three primary dimensions: economically, devaluing creative labor and disrupting traditional business models; socially, eroding trust and accelerating an epistemic crisis; and psychologically, inducing cognitive fatigue and undermining critical thinking skills. The principal driving forces stem from the convergence of easily accessible technology, an attention economy that rewards volume over quality, and platform algorithms that inadvertently amplify low-quality content. Effective integrated countermeasures must encompass three areas: developing AI detection tools and content provenance standards at the technological level; reforming algorithms and enacting transparency legislation at the policy level; and promoting digital and AI literacy at the educational level, in order to cultivate a sustainable and trustworthy digital information ecosystem.