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The Value of AI-Generated Metadata for UGC Platforms: Evidence from a Large-scale Field Experiment

Author

Listed:
  • Xinyi Zhang
  • Chenshuo Sun
  • Renyu Zhang
  • Khim-Yong Goh

Abstract

AI-generated content (AIGC), such as advertisement copy, product descriptions, and social media posts, is becoming ubiquitous in business practices. However, the value of AI-generated metadata, such as titles, remains unclear on user-generated content (UGC) platforms. To address this gap, we conducted a large-scale field experiment on a leading short-video platform in Asia to provide about 1 million users access to AI-generated titles for their uploaded videos. Our findings show that the provision of AI-generated titles significantly boosted content consumption, increasing valid watches by 1.6% and watch duration by 0.9%. When producers adopted these titles, these increases jumped to 7.1% and 4.1%, respectively. This viewership-boost effect was largely attributed to the use of this generative AI (GAI) tool increasing the likelihood of videos having a title by 41.4%. The effect was more pronounced for groups more affected by metadata sparsity. Mechanism analysis revealed that AI-generated metadata improved user-video matching accuracy in the platform's recommender system. Interestingly, for a video for which the producer would have posted a title anyway, adopting the AI-generated title decreased its viewership on average, implying that AI-generated titles may be of lower quality than human-generated ones. However, when producers chose to co-create with GAI and significantly revised the AI-generated titles, the videos outperformed their counterparts with either fully AI-generated or human-generated titles, showcasing the benefits of human-AI co-creation. This study highlights the value of AI-generated metadata and human-AI metadata co-creation in enhancing user-content matching and content consumption for UGC platforms.

Suggested Citation

  • Xinyi Zhang & Chenshuo Sun & Renyu Zhang & Khim-Yong Goh, 2024. "The Value of AI-Generated Metadata for UGC Platforms: Evidence from a Large-scale Field Experiment," Papers 2412.18337, arXiv.org.
  • Handle: RePEc:arx:papers:2412.18337
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    References listed on IDEAS

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    1. Jiankun Sun & Dennis J. Zhang & Haoyuan Hu & Jan A. Van Mieghem, 2022. "Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations," Management Science, INFORMS, vol. 68(2), pages 846-865, February.
    2. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
    3. Mikhail Lysyakov & Siva Viswanathan, 2023. "Threatened by AI: Analyzing Users’ Responses to the Introduction of AI in a Crowd-Sourcing Platform," Information Systems Research, INFORMS, vol. 34(3), pages 1191-1210, September.
    4. Tianshu Sun & Lanfei Shi & Siva Viswanathan & Elena Zheleva, 2019. "Motivating Effective Mobile App Adoptions: Evidence from a Large-Scale Randomized Field Experiment," Information Systems Research, INFORMS, vol. 30(2), pages 523-539, June.
    5. Ni Huang & Gordon Burtch & Bin Gu & Yili Hong & Chen Liang & Kanliang Wang & Dongpu Fu & Bo Yang, 2019. "Motivating User-Generated Content with Performance Feedback: Evidence from Randomized Field Experiments," Management Science, INFORMS, vol. 65(1), pages 327-345, January.
    6. Cole, Rebel A. & Sokolyk, Tatyana, 2018. "Debt financing, survival, and growth of start-up firms," Journal of Corporate Finance, Elsevier, vol. 50(C), pages 609-625.
    7. Maytal Saar-Tsechansky & Prem Melville & Foster Provost, 2009. "Active Feature-Value Acquisition," Management Science, INFORMS, vol. 55(4), pages 664-684, April.
    8. Xingyue (Luna) Zhang & James A. Dearden & Yuliang Yao, 2022. "Let them stay or let them go? Online retailer pricing strategy for managing stockouts," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4173-4190, November.
    9. Zhen Fang & Ming Fan & Apurva Jain, 2023. "Content proliferation and narrowcasting in the age of streaming media," Production and Operations Management, Production and Operations Management Society, vol. 32(10), pages 3295-3310, October.
    10. Ni Huang & Probal Mojumder & Tianshu Sun & Jinchi Lv & Joseph M. Golden, 2021. "Not Registered? Please Sign Up First: A Randomized Field Experiment on the Ex Ante Registration Request," Information Systems Research, INFORMS, vol. 32(3), pages 914-931, September.
    11. Scott C. Ellis & Shashank Rao & Dheeraj Raju & Thomas J. Goldsby, 2018. "RFID Tag Performance: Linking the Laboratory to the Field through Unsupervised Learning," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1834-1848, October.
    12. Gordon Burtch & Qinglai He & Yili Hong & Dokyun Lee, 2022. "How Do Peer Awards Motivate Creative Content? Experimental Evidence from Reddit," Management Science, INFORMS, vol. 68(5), pages 3488-3506, May.
    13. Zikun Ye & Dennis J. Zhang & Heng Zhang & Renyu Zhang & Xin Chen & Zhiwei Xu, 2023. "Cold Start to Improve Market Thickness on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments," Management Science, INFORMS, vol. 69(7), pages 3838-3860, July.
    14. Ruomeng Cui & Jun Li & Dennis J. Zhang, 2020. "Reducing Discrimination with Reviews in the Sharing Economy: Evidence from Field Experiments on Airbnb," Management Science, INFORMS, vol. 66(3), pages 1071-1094, March.
    15. Jiaxu Peng & Jungpil Hahn & Ke-Wei Huang, 2023. "Handling Missing Values in Information Systems Research: A Review of Methods and Assumptions," Information Systems Research, INFORMS, vol. 34(1), pages 5-26, March.
    16. Saravanan Kesavan & Tarun Kushwaha, 2020. "Field Experiment on the Profit Implications of Merchants’ Discretionary Power to Override Data-Driven Decision-Making Tools," Management Science, INFORMS, vol. 66(11), pages 5182-5190, November.
    17. Martin Reisenbichler & Thomas Reutterer & David A. Schweidel & Daniel Dan, 2022. "Frontiers: Supporting Content Marketing with Natural Language Generation," Marketing Science, INFORMS, vol. 41(3), pages 441-452, May.
    18. Dandan Qiao & Shun-Yang Lee & Andrew B. Whinston & Qiang Wei, 2020. "Financial Incentives Dampen Altruism in Online Prosocial Contributions: A Study of Online Reviews," Information Systems Research, INFORMS, vol. 31(4), pages 1361-1375, December.
    19. Sriram Narayanan & Sridhar Balasubramanian & Jayashankar M. Swaminathan, 2009. "A Matter of Balance: Specialization, Task Variety, and Individual Learning in a Software Maintenance Environment," Management Science, INFORMS, vol. 55(11), pages 1861-1876, November.
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    2. Chenshuo Sun, 2025. "How Does Prepopulating Search Bars with Keywords Affect Online Consumer Behavior? A Field Experiment," Marketing Science, INFORMS, vol. 44(6), pages 1217-1231, November.

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