The uneven rise of AI-generated web content
Authors: Adam Visokay, Soumyakanti Pan, Molly Griston, Polina Iasakova, Mark Nepf, Denis Peskoff, Riya Sinha, Jikai Sun, Isabelle Thiara, Letao Wang, Yiwei Wu, Lauren Yan, Tyler H. McCormick
Abstract
In less than five years, generative artificial intelligence (AI) has dramatically reshaped the public-facing web. Using a sample of more than 5 million crawlable webpages, we demonstrate that the share containing AI-generated text has increased precipitously, but unevenly. Our analyses reveal substantial differences in AI composition across language and domain categories (e.g., .edu, .com, or .gov). A higher fraction of English-language webpages contain AI-generated text compared to pages in other languages, and prevalence is substantially lower on .gov and .edu domains compared to .com. These distinctions persist even after accounting for compositional effects. We also find heterogeneity based on a page's position in the connectivity structure of the web. AI-generated text is most common and has increased most rapidly in pages that are the least central in the graph, which drives much of the overall growth in AI-generated text. A slower increase has taken place for more central pages, with the most central tier emerging as the least likely to contain AI-generated text. Methodologically, we use multiple AI detection tools, and generate estimates using a Bayesian hierarchical model that incorporates corrections for the detectors' statistical properties (both false positives and false negatives) and allows for different detector performance across groups, while propagating uncertainty in detector error rates into prevalence estimates. Our work suggests that AI is a prominent, but varied, contributor to public information and internet-based communication.
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Delving into: the quantification of Ai-generated content on the internet (synthetic data)D. H. Spennemann · 2025 · arXiv.org · preprint, not peer reviewed · cited 23 times · Not summarized yet
The study estimates how much web text is AI-generated, using ChatGPT-associated keywords rather than detector-calibrated sampling.
Abstract
While it is increasingly evident that the internet is becoming saturated with content created by generated Ai large language models, accurately measuring the scale of this phenomenon has proven challenging. By analyzing the frequency of specific keywords commonly used by ChatGPT, this paper demonstrates that such linguistic markers can effectively be used to esti-mate the presence of generative AI content online. The findings suggest that at least 30% of text on active web pages originates from AI-generated sources, with the actual proportion likely ap-proaching 40%. Given the implications of autophagous loops, this is a sobering realization.
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The Impact of AI-Generated Text on the InternetJ. Dolezal, Sawood Alam, Mark Graham et al. · 2026 · arXiv.org · preprint, not peer reviewed · cited 9 times · Not summarized yet
The study estimates the prevalence of AI-generated or AI-edited text on websites from 2022 to 2025 using an Internet Archive sample and a text detector.
Abstract
The proliferation of AI-generated and AI-assisted text on the internet is feared to contribute to a degradation in semantic and stylistic diversity, factual accuracy, and other negative developments (sometimes subsumed under the Dead Internet Theory). What has hindered answering these questions is that it has not been understood just how much of the internet is actually AI-generated or AI-edited. To this end, we construct a representative sample of websites published on the internet between 2022 and 2025 using the Internet Archive, and apply a state-of-the-art AI text detector on them. We find that by mid-2025, roughly 35% of newly published websites were classified as AI-generated or AI-ass…
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The Rise of AI-Generated Content in WikipediaCreston Brooks, Samuel Eggert, Denis Peskoff · 2024 · Proceedings of the First Workshop on Advancing Natural Language Processing for Wikipedia · cited 44 times · Not summarized yet
The study measures AI-generated text in recently created Wikipedia pages with two detectors, rather than across web domains.
Abstract
The rise of AI-generated content in popular information sources raises significant concerns about accountability, accuracy, and bias amplification. Beyond directly impacting consumers, the widespread presence of this content poses questions for the long-term viability of training language models on vast internet sweeps. We use GPTZero, a proprietary AI detector, and Binoculars, an open-source alternative, to establish lower bounds on the presence of AI-generated content in recently created Wikipedia pages. Both detectors reveal a marked increase in AI-generated content in recent pages compared to those from before the release of GPT-3.5. With thresholds calibrated to achieve a 1% false posit…
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A Study on Influence of Generative AI on the Future of InternetBinoy Manmadhan, M. Rodrigues · 2025 · The Voice of Creative Research · Not summarized yet
The study discusses generative AI’s effects on the internet, including content creation and concerns about misinformation and online information reliability.
Abstract
The internet has evolved from its origins as a military and academic communications project initiated by the Advanced Research Projects Agency (ARPA) into a decentralized global network for knowledge sharing, communication, and commerce. Today with the rapid advancements in artificial intelligence (AI), Generative AI the branch of AI capable of producing text, images, videos, and other media—has emerged as a trans- formative force across multiple industries by assisting professionals in various tasks. While this technology enhances content creation and problem-solving capabilities, it also raises concerns regarding misinformation, intellectual property, and the reliability of online informat…
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Detecting AI Generated TextI. Chandana, Oruganti Mariya Reshma, Nerella Geetha Sree et al. · 2024 · 2024 2nd World Conference on Communication & Computing (WCONF) · cited 4 times · Not summarized yet
The project trains machine-learning models to distinguish AI-generated from human-written text, while this paper uses detectors to estimate web prevalence.
Abstract
The exponential progression in artificial intelligence (AI) technologies has spearheaded the evolution towards proliferation of AI-generated text across various online platforms. This phenomenon poses significant challenges in maintaining the integrity of information dissemination and combating misinformation. In response, this project proposes a comprehensive approach to detecting AI-generated text through ML methods. By leveraging a diverse dataset of both AI-generated and human-generated text, the project aims to develop robust models capable of accurately discerning AI-generated content. The methodology involves feature engineering, model training, and validation using state-of-the-art N…
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The study examines AI-generated political posts on Bluesky and their correlations with persuasion and disinformation, rather than prevalence across web pages.
Abstract
The rapid proliferation of large language models (LLMs) and generative AI in creating fluent political messaging has raised significant concerns regarding persuasion and the spread of disinformation. This study analyzes AI-generated content and its correlation with these phenomena using a 24-month dataset of 120,000 U.S. politics-related posts from the Bluesky platform. Utilizing OSM-Det for AI detection and PCoT for quantifying persuasion and disinformation, we examine the shifting landscape of automated political discourse. Our findings reveal: (i) a substantial increase in both AI-generated content and disinformation; (ii) a strong positive correlation between AI-generated output and disi…
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Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability IndexMegha Chakraborty, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman et al. · 2023 · Computation and Language · cited 35 times · AI summary available
The study examines how detectable AI-generated text is and introduces an AI Detectability Index, addressing detector performance relevant to prevalence estimation.
Abstract
With the rise of prolific ChatGPT, the risk and consequences of AI-generated text has increased alarmingly. To address the inevitable question of ownership attribution for AI-generated artifacts, the US Copyright Office released a statement stating that 'If a work's traditional elements of authorship were produced by a machine, the work lacks human authorship and the Office will not register it'. Furthermore, both the US and the EU governments have recently drafted their initial proposals regarding the regulatory framework for AI. Given this cynosural spotlight on generative AI, AI-generated text detection (AGTD) has emerged as a topic that has already received immediate attention in researc…
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AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and PropositionsJianxin Li, Liang Qu, Tao-Tao Cai et al. · 2025 · Knowledge-Based Systems · cited 22 times · Not summarized yet
The paper reviews AIGC applications, research trends, and challenges across domains including marketing, education, and public health.
Abstract
Artificial Intelligence Generated Content (AIGC) has rapidly emerged with the capability to generate different forms of content, including text, images, videos, and other modalities, which can achieve a quality similar to content created by humans. As a result, AIGC is now widely applied across various domains such as digital marketing, education, and public health, and has shown promising results by enhancing content creation efficiency and improving information delivery. However, there are few studies that explore the latest progress and emerging challenges of AIGC across different domains. To bridge this gap, this paper brings together 16 scholars from multiple disciplines to provide a cr…
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Updated on 2 November 2026.