Understanding Social Media Users' Perspectives for Content Reuse

Social media users create immense value for various technologies such as search engines, content platforms, and most recently, generative AI models. Among social media users, those who produce content professionally, such as artists and creative workers, have expressed strong concerns about unauthorized reuse of their content; however, it remains unclear how the vast majority of casual users perceive content reuse, in particular for AI training. To gain a comprehensive understanding of users' perspectives, we surveyed 478 Instagram users from a university network about how much they value their content for commercial reuse and their acceptance of specific content reuse scenarios. We found that under reverse auction, in which the lowest bidder wins, respondents' evaluation of their content for commercial reuse is highly skewed, with a median of $62 and a long tail of more than thousands of dollars. A nontrivial percentage of respondents found content reuse for search engine display, education, and academic research acceptable. Conversely, content reuse for commercial or non-profit generative AI models was a negotiable scenario for most respondents. Most notably, compensation was likely to make almost all scenarios more acceptable to users. Our work highlights the diverse, contextually dependent perspectives that users have. We discuss how design, policy, and market practices might support contextually aware content reuse.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2026-03-31
DOI
https://doi.org/10.1145/3799454
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Understanding Social Media Users' Perspectives for Content Reuse

Riya Sinha, Hanlin Li
Proceedings of the ACM on Human-Computer Interaction
Ethics and Social Impacts of AI
article

Understanding Social Media Users' Perspectives for Content Reuse

Riya Sinha, Hanlin Li
article en

Abstract

Social media users create immense value for various technologies such as search engines, content platforms, and most recently, generative AI models. Among social media users, those who produce content professionally, such as artists and creative workers, have expressed strong concerns about unauthorized reuse of their content; however, it remains unclear how the vast majority of casual users perceive content reuse, in particular for AI training. To gain a comprehensive understanding of users' perspectives, we surveyed 478 Instagram users from a university network about how much they value their content for commercial reuse and their acceptance of specific content reuse scenarios. We found that under reverse auction, in which the lowest bidder wins, respondents' evaluation of their content for commercial reuse is highly skewed, with a median of $62 and a long tail of more than thousands of dollars. A nontrivial percentage of respondents found content reuse for search engine display, education, and academic research acceptable. Conversely, content reuse for commercial or non-profit generative AI models was a negotiable scenario for most respondents. Most notably, compensation was likely to make almost all scenarios more acceptable to users. Our work highlights the diverse, contextually dependent perspectives that users have. We discuss how design, policy, and market practices might support contextually aware content reuse.

Proceedings of the ACM on Human-Computer InteractionVol. 10(1)
The University of Texas at Austin (US)
Openalex Percentile: Top 27%
Ethics and Social Impacts of AI
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