Algorithmic curation systems amplify AI-generated content (AIGC) for viral spread across platforms.
Algorithmic curation systems amplify AI-generated content (AIGC) by prioritizing high-engagement items regardless of their origin, enabling synthetic text, images, and videos to achieve rapid, large-scale reach across platforms . These systems exploit existing digital infrastructures such as social media feeds, search engines, and recommender systems, allowing AIGC to spread virally within hours through feedback loops of clicks, shares, and watch time . Empirical studies show that AI-generated content often spreads faster than human-created content due to its optimized linguistic fluency and visual realism, which increase click-through and sharing rates . This cross-platform diffusion is further accelerated by automated reposting and embedding, which fragment provenance and hinder coordinated moderation efforts . As a result, AIGC can quickly migrate from niche communities to mainstream channels, amplifying both beneficial applications and risks such as misinformation proliferation .
This research provides a comprehensive analysis of the convergence between generative AI and algorithmic curation, examining how AIGC proliferates across various digital ecosystems. It highlights a distinct feedback loop where algorithmic systems, designed to maximize engagement, preferentially amplify AI-generated material, accelerating its viral potential across heterogeneous platforms. The study categorizes current research trends, mapping the trajectory of AIGC from niche applications to a dominant force in cross-domain content dissemination, while scrutinizing the technical mechanisms that allow synthetic media to outpace human-generated content in reach and velocity.
A critical contribution of this work is the identification of specific challenges arising from the symbiosis of generative models and recommendation engines. The authors elucidate technical risks such as the rapid homogenization of content, the saturation of information channels with synthetic media, and the ensuing difficulty in maintaining information integrity. Furthermore, the paper propositions architectural and policy-based frameworks to mitigate these issues, suggesting that future curation algorithms must integrate robust AIGC detection and provenance verification mechanisms to prevent the degradation of content quality and the manipulation of user engagement metrics.
This material is essential reading for researchers and engineers working on recommender systems and content moderation, as it exposes the systemic vulnerabilities introduced by unchecked AIGC proliferation. By framing the issue not merely as a content generation problem but as a curation and amplification problem, it shifts the focus toward the responsibility of platform infrastructure in managing the digital commons. Understanding these dynamics is vital for preserving the utility and trustworthiness of online information environments against the backdrop of increasingly sophisticated synthetic content.
This study explores the growing influence of AI-generated content (AIGC) across diverse domains, examining how algorithmic curation systems amplify its viral propagation on digital platforms. The paper synthesizes recent research trends, highlighting AIGC’s applications in areas such as marketing, entertainment, and misinformation, while identifying key challenges like detectability, ethical risks, and platform governance. It proposes frameworks for mitigating harm while leveraging AIGC’s potential, emphasizing the need for adaptive detection tools and transparent curation policies.
A key insight is the cross-domain interplay of AIGC, where content generated for one purpose (e.g., synthetic media in advertising) can inadvertently spread misinformation or manipulate engagement metrics in unrelated contexts. The authors argue for a multi-stakeholder approach, involving technologists, policymakers, and content creators, to balance innovation with accountability. The work is significant for researchers and practitioners navigating the rapid evolution of generative AI, offering actionable propositions for responsible deployment in real-world systems.
Why it matters: As AIGC becomes ubiquitous, this paper provides a critical lens on its unintended consequences, particularly in algorithmically driven ecosystems. By mapping challenges (e.g., deepfakes, bias amplification) and proposing mitigation strategies (e.g., hybrid human-AI moderation), it bridges the gap between technical feasibility and societal impact—a crucial step toward sustainable AI integration.
Source: [arXiv:2509.11151v1](https://arxiv.org/html/2509.11151v1)