# The Impact of Generative AI on Social Media: An Experimental Study

arXiv: https://arxiv.org/abs/2506.14295 · HTML: https://mlsanity.com/paper/2506.14295/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2025.

## Built on by (most cited first, 6 of 6)

- [Generative Exaggeration in LLM Social Agents: Consistency, Bias, and Toxicity](https://mlsanity.com/paper/2507.00657/) (2025)
- [Can Lessons From Human Teams Be Applied to Multi-Agent Systems? The Role of Structure, Diversity, and Interaction Dynamics](https://mlsanity.com/paper/2510.07488/) (2025)
- [User Negotiations of Authenticity, Ownership, and Governance on AI-Generated Video Platforms: Evidence from Sora](https://mlsanity.com/paper/2512.05519/) (2025)
- [Identifying Models Behind Text-to-Image Leaderboards](https://mlsanity.com/paper/2601.09647/) (2026)
- [Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology](https://mlsanity.com/paper/2604.01690/) (2026)
- [Personalized Image Generation with Reasoning and Reflection](https://mlsanity.com/paper/2610.00737/) (2026)

## Abstract (arXiv)

Generative Artificial Intelligence (AI) tools are increasingly deployed across social media platforms, yet their implications for user behavior and experience remain understudied, particularly regarding two critical dimensions: (1) how AI tools affect the behaviors of content producers in a social media context, and (2) how content generated with AI assistance is perceived by users. To fill this gap, we conduct a controlled experiment with a representative sample of 680 U.S. participants in a realistic social media environment. The participants are randomly assigned to small discussion groups, each consisting of five individuals in one of five distinct experimental conditions: a control group and four treatment groups, each employing a unique AI intervention-chat assistance, conversation starters, feedback on comment drafts, and reply suggestions. Our findings highlight a complex duality: some AI-tools increase user engagement and volume of generated content, but at the same time decrease the perceived quality and authenticity of discussion, and introduce a negative spill-over effect on conversations. Based on our findings, we propose four design principles and recommendations aimed at social media platforms, policymakers, and stakeholders: ensuring transparent disclosure of AI-generated content, designing tools with user-focused personalization, incorporating context-sensitivity to account for both topic and user intent, and prioritizing intuitive user interfaces. These principles aim to guide an ethical and effective integration of generative AI into social media.