推动弱者：揭示主流流媒体访问如何推动直播娱乐平台上新兴流媒体的收入

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems Pub Date : 2025-07-25 DOI:10.1016/j.dss.2025.114511

Huijing Guo , Xin Bao , Le Wang , Xin (Robert) Luo

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引用次数: 0

摘要

直播娱乐（LSE）平台在实时社交互动方面越来越受欢迎。虽然高地位的演员（流行的流媒体）吸引了大量的观众，但新的流媒体经常在知名度和收入方面挣扎。本研究以社会资本理论和激励理论为框架，探讨了社会资本传播对新兴流媒体直播收入的影响。我们在LSE的一个主要平台上分析了两周内来自52010个新兴流媒体的数据。研究表明，来自老牌主播的访问量显著增加了新主播的收入。当新的主播过去表现良好，并且属于顶级公会，并且访问的老牌主播有良好的表现记录并在访问期间积极互动时，这种积极的影响就会明显增强。我们的研究结果通过强调知名主播的支持作用，为伦敦政治经济学院的平台研究做出了贡献。这些见解可以帮助平台制定战略，增强平台活力，使内容多样化，支持新兴流媒体的发展，并培养一个更可持续的流媒体生态系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Boosting the underdogs: Unraveling how prevailing streamer visits drive revenue for emerging streamers on livestreaming entertainment platforms

Livestreaming entertainment (LSE) platforms have become increasingly popular for real-time social interaction. While high-status actors (prevailing streamers) attract large audiences, new streamers often struggle with visibility and earnings. This study examines how social capital transmission from high-status actors affect emerging streamers' live revenue, using Social Capital Theory and Arousal Theories as frameworks. We analyzed data from 52,010 emerging streamers over two weeks on a major LSE platform. The research shows that visits from established streamers significantly increase new streamers' revenue. This positive effect is notably stronger when new streamers have shown good past performance and belong to top guilds and visiting established streamers have strong performance records and actively interact during their visits. Our findings contribute to LSE platform research by highlighting the supportive role of established streamers. These insights can help platforms develop strategies to enhance platform vitality, diversify content, support emerging streamers' growth, and foster a more sustainable streaming ecosystem.

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来源期刊

Decision Support Systems 工程技术-计算机：人工智能

CiteScore

14.70

自引率

6.70%

发文量

119

审稿时长

13 months

期刊介绍： The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).