The Impact of Social Nudges on User-Generated Content for Social Network Platforms

• 成果类型: Article
• 署名作者: Zeng, Zhiyu; Dai, Hengchen; Zhang, Dennis J.; Zhang, Heng; Zhang, Renyu; Xu, Zhiwei; Shen, Zuo-Jun Max
• 署名单位: Tsinghua University; University of California System; University of California Los Angeles; Washington University (WUSTL); Arizona State University; Arizona State University-Tempe; Chinese University of Hong Kong; University of California System; University of California Berkeley; University of California System; University of California Berkeley
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.4622
• 发表日期: 2023
• 页码: 5189-5208
• 关键词: content production; Platform Operations; social network; field experiment; information-based intervention

摘要

Content-sharing social network platforms rely heavily on user-generated content to attract users and advertisers, but they have limited authority over content provision. We develop an intervention that leverages social interactions between users to stimulate content production. We study social nudges, whereby users connected with a content provider on a platform encourage that provider to supply more content. We conducted a randomized field experiment (N = 993,676) on a video-sharing social network platform where treatment providers could receive messages from other users encouraging them to produce more, but control providers could not. We find that social nudges not only immediately boosted video supply by 13.21% without changing video quality but also, increased the number of nudges providers sent to others by 15.57%. Such production-boosting and diffusion effects, although declining over time, lasted beyond the day of receiving nudges and were amplified when nudge senders and recipients had stronger ties. We replicate these results in a second experiment. To estimate the overall production boost over the entire network and guide platforms to utilize social nudges, we combine the experimental data with a social network model that captures the diffusion and over-time effects of social nudges. We showcase the importance of considering the network effects when estimating the impact of social nudges and optimizing platform operations regarding social nudges. Our research highlights the value of leveraging co-user influence for platforms and provides guidance for future research to incorporate the diffusion of an intervention into the estimation of its impacts within a social network.