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The Impact of Generative AI on Social Media: An Experimental Study (2506.14295v1)

Published 17 Jun 2025 in cs.HC

Abstract: Generative 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.

Summary

  • The paper demonstrates that AI tools significantly boost user engagement in social media, as observed in a controlled experiment with 680 participants.
  • Methodologically, the study employed random assignment to control and four treatment groups using various AI interventions like chat assistance and reply suggestions.
  • The findings imply that while AI-generated content increases discussion volume, it may also reduce perceived authenticity and content quality.

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

The integration of generative AI tools into social media has sparked considerable interest and concern within both academic and industry circles. This paper, titled "The Impact of Generative AI on Social Media: An Experimental Study," offers an empirical investigation into the nuanced effects of AI-assisted content creation on user behavior and perceptions within a controlled social media environment.

Methodology

The paper employs a rigorous experimental method involving 680 U.S. participants. These participants were divided into small discussion groups within a custom-built social media platform that mirrored real-world online forums. The participants were randomly allocated into one control group and four treatment groups using different AI tools: Chat Assistance, Conversation Starters, Feedback on Comment Drafts, and AI-generated Reply Suggestions. Each group engaged in discussions on various topics, each reflecting different levels of sensitivity and complexity, over three rounds, each lasting ten minutes.

Key Findings

The paper reveals a dual effect of generative AI tools on social media interactions. From the perspective of content producers, AI tools significantly increased user engagement and the volume of content generated. Participants using AI tools reported a greater willingness to participate in online discussions, particularly noted in the Chat and Suggestions conditions. However, the characteristics of AI-generated content, often perceived as lacking in authenticity and quality, resulted in a cautious reception among content consumers. None of the AI interventions consistently improved user perceptions compared to the control group, with AI tools generally correlating with increased 'dislikes' and diminished informativeness ascribed to discussions.

Implications and Design Principles

In response to the insights gained from the experiment, the authors propose design principles targeting social media platforms and policy-makers. These include ensuring transparent disclosure of AI-generated content, designing AI systems with personalized user experiences, embedding contextual awareness within AI systems, and developing intuitive user interfaces. These design principles aim at harnessing the benefits of AI tools while mitigating negative impacts such as the creation of "semantic garbage," as termed by the authors.

Future Directions

The paper opens avenues for further research into the long-term effects of AI-assisted content on social media ecosystems, encouraging analyses across broader, more varied digital environments. It advocates for ongoing refinement of AI systems to better align with user needs and conversational contexts, potentially transforming AI's role from a passive to a more engaging and constructive participant in social media interactions.

In summary, this research makes important contributions to the understanding of generative AI's influence on social media dynamics, highlighting the need for ethical and strategically nuanced AI integration into digital discourse platforms. The authors stress that while AI holds the potential to broaden participation and engagement, careful management is crucial to maintain the quality and authenticity that underpin meaningful online interactions.

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