# Examining the Prevalence and Dynamics of AI-Generated Media in Art Subreddits arXiv: https://arxiv.org/abs/2410.07302 ยท HTML: https://mlsanity.com/paper/2410.07302/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 70 references that are in the map. - First posted on arXiv 9 Oct 2024. ## Builds on (most cited first, 23 of 23) - [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018) - [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020) - [Cross-Modal Contrastive Learning for Text-to-Image Generation](https://mlsanity.com/paper/2101.04702/) (2021) - [Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews](https://mlsanity.com/paper/2403.07183/) (2024) - [Human heuristics for AI-generated language are flawed](https://mlsanity.com/paper/2206.07271/) (2022) - [Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks](https://mlsanity.com/paper/2306.07899/) (2023) - [The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates](https://mlsanity.com/paper/2405.02150/) (2024) - [Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images](https://mlsanity.com/paper/2304.13023/) (2023) - [Online Detection of AI-Generated Images](https://mlsanity.com/paper/2310.15150/) (2023) - [Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites](https://mlsanity.com/paper/2305.09820/) (2023) - [Generative AI Text Classification using Ensemble LLM Approaches](https://mlsanity.com/paper/2309.07755/) (2023) - [Pushing The Limit of LLM Capacity for Text Classification](https://mlsanity.com/paper/2402.07470/) (2024) - [Knowledge Distillation in Automated Annotation: Supervised Text Classification with LLM-Generated Training Labels](https://mlsanity.com/paper/2406.17633/) (2024) - [Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?](https://mlsanity.com/paper/2402.03214/) (2024) - [Understanding the Impact of AI Generated Content on Social Media: The Pixiv Case](https://mlsanity.com/paper/2402.18463/) (2024) - [Making Online Communities 'Better': A Taxonomy of Community Values on Reddit](https://mlsanity.com/paper/2109.05152/) (2021) - [AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images](https://mlsanity.com/paper/2404.14244/) (2024) - ["There Has To Be a Lot That We're Missing": Moderating AI-Generated Content on Reddit](https://mlsanity.com/paper/2311.12702/) (2023) - [QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums](https://mlsanity.com/paper/2405.05345/) (2024) - [Human Perception of LLM-generated Text Content in Social Media Environments](https://mlsanity.com/paper/2409.06653/) (2024) - [Community Archetypes: An Empirical Framework for Guiding Research Methodologies to Reflect User Experiences of Sense of Virtual Community](https://mlsanity.com/paper/2310.02515/) (2023) - [Why do people participate in small online communities?](https://mlsanity.com/paper/2108.04282/) (2021) - [Bystanders of Online Moderation: Examining the Effects of Witnessing Post-Removal Explanations](https://mlsanity.com/paper/2309.08361/) (2023) ## Built on by (most cited first, 8 of 8) - [AI use in American newspapers is widespread, uneven, and rarely disclosed](https://mlsanity.com/paper/2510.18774/) (2025) - [GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment](https://mlsanity.com/paper/2604.25370/) (2026) - [What happens when generative AI models train recursively on each others' outputs?](https://mlsanity.com/paper/2505.21677/) (2025) - [The Impact of AI-Generated Text on the Internet](https://mlsanity.com/paper/2604.26965/) (2026) - [Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit](https://mlsanity.com/paper/2510.07226/) (2025) - [On the Dangers of Bootstrapping Generation for Continual Learning and Beyond](https://mlsanity.com/paper/2512.11867/) (2025) - [Identifying Models Behind Text-to-Image Leaderboards](https://mlsanity.com/paper/2601.09647/) (2026) - [ChatGPT Images 2.5 in the Wild: A Launch-Period Dataset and Detector Evaluation](https://mlsanity.com/paper/2609.15100/) (2026) ## Abstract (arXiv) Broadly accessible generative AI models like Dall-E have made it possible for anyone to create compelling visual art. In online communities, the introduction of AI-generated content (AIGC) may impact social dynamics, for example causing changes in who is posting content, or shifting the norms or the discussions around the posted content if posts are suspected of being generated by AI. We take steps towards examining the potential impact of AIGC on art-related communities on Reddit. We distinguish between communities that disallow AI content and those without such a direct policy. We look at image-based posts in these communities where the author transparently shares that the image was created by AI, and at comments in these communities that suspect or accuse authors of using generative AI. We find that AI posts (and accusations) have played a surprisingly small part in these communities through the end of 2023, accounting for fewer than 0.5% of the image-based posts. However, even as the absolute number of author-labeled AI posts dwindles over time, accusations of AI use remain more persistent. We show that AI content is more readily used by newcomers and may help increase participation if it aligns with community rules. However, the tone of comments suspecting AI use by others has become more negative over time, especially in communities that do not have explicit rules about AI. Overall, the results show the changing norms and interactions around AIGC in online communities designated for creativity.