Examining the Prevalence and Dynamics of AI-Generated Media in Art Subreddits
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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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 23 of its 70 references that are in the map. First posted on arXiv 9 Oct 2024.
Builds on
23 of 70 references.
References · 23
- 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
- 2020The Pushshift Reddit Datasetcited by 289
- 2021Cross-Modal Contrastive Learning for Text-to-Image Generationcited by 259
- 2024Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviewscited by 160
- 2022Human heuristics for AI-generated language are flawedcited by 104
- 2023Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Taskscited by 102
- 2024The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Ratescited by 66
- 2023Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Imagescited by 65
- 2023Online Detection of AI-Generated Imagescited by 55
- 2023Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websitescited by 37
- 2023Generative AI Text Classification using Ensemble LLM Approachescited by 29
- 2024Pushing The Limit of LLM Capacity for Text Classificationcited by 28
11 more
- 2024Knowledge Distillation in Automated Annotation: Supervised Text Classification with LLM-Generated Training Labelscited by 25
- 2024Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?cited by 21
- 2024Understanding the Impact of AI Generated Content on Social Media: The Pixiv Casecited by 13
- 2021Making Online Communities 'Better': A Taxonomy of Community Values on Redditcited by 10
- 2024AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Imagescited by 10
- 2024QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forumscited by 8
- 2023"There Has To Be a Lot That We're Missing": Moderating AI-Generated Content on Redditcited by 7
- 2024Human Perception of LLM-generated Text Content in Social Media Environmentscited by 7
- 2023Community Archetypes: An Empirical Framework for Guiding Research Methodologies to Reflect User Experiences of Sense of Virtual Communitycited by 5
- 2021Why do people participate in small online communities?cited by 3
- 2023Bystanders of Online Moderation: Examining the Effects of Witnessing Post-Removal Explanationscited by 2
Built on by
Citing papers per year: 2025: 4; 2026: 4.
8 papers.
- 2025AI use in American newspapers is widespread, uneven, and rarely disclosedcited by 9
- 2026GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deploymentcited by 8
- 2026The Impact of AI-Generated Text on the Internetcited by 6
- 2025What happens when generative AI models train recursively on each others' outputs?cited by 5
- 2025Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Redditcited by 4
- 2025On the Dangers of Bootstrapping Generation for Continual Learning and Beyondcited by 0
- 2026Identifying Models Behind Text-to-Image Leaderboardscited by 0
- 2026ChatGPT Images 2.5 in the Wild: A Launch-Period Dataset and Detector Evaluationcited by 0
- 2026ChatGPT Images 2.5 in the Wild: A Launch-Period Dataset and Detector Evaluation14 Sep 2026
- 2026GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment28 Apr 2026
- 2026The Impact of AI-Generated Text on the Internet14 Apr 2026
- 2026Identifying Models Behind Text-to-Image Leaderboards14 Jan 2026
- 2025On the Dangers of Bootstrapping Generation for Continual Learning and Beyond5 Dec 2025
- 2025AI use in American newspapers is widespread, uneven, and rarely disclosed21 Oct 2025
- 2025Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit8 Oct 2025
- 2025What happens when generative AI models train recursively on each others' outputs?27 May 2025