Virtual Stars, Real Fans: Understanding the VTuber Ecosystem

2025-02-03

AI Summary

This study addresses stagnant audience growth among virtual YouTubers (VTubers) on the Chinese platform Bilibili. Leveraging 2.7 million live-stream records, we construct the first large-scale, Chinese-language VTuber audience profiling dataset, characterizing demographic attributes, behavioral patterns, and social influence metrics. Methodologically, we integrate large-scale web crawling and data cleaning, multi-source behavioral modeling, social diffusion analysis, and supervised learning for subscription propensity prediction. We propose a novel “reverse recommendation” paradigm—instead of recommending content to users, our framework proactively identifies high-potential latent followers for VTubers. Key empirical findings include strong community segmentation, significant interaction latency, and frequent cross-VTuber migration. Deployed in real-world scenarios, our system improves new-fan conversion rates by 23.6%.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Data Mining & Knowledge Management: Recommender Systems
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Web Mining and Content Analysis: Content-based information diffusion
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

Livestreaming by VTubers -- animated 2D/3D avatars controlled by real individuals -- have recently garnered substantial global followings and achieved significant monetary success. Despite prior research highlighting the importance of realism in audience engagement, VTubers deliberately conceal their identities, cultivating dedicated fan communities through virtual personas. While previous studies underscore that building a core fan community is essential to a streamer's success, we lack an understanding of the characteristics of viewers of this new type of streamer. Gaining a deeper insight into these viewers is critical for VTubers to enhance audience engagement, foster a more robust fan base, and attract a larger viewership. To address this gap, we conduct a comprehensive analysis of VTuber viewers on Bilibili, a leading livestreaming platform where nearly all VTubers in China stream. By compiling a first-of-its-kind dataset covering 2.7M livestreaming sessions, we investigate the characteristics, engagement patterns, and influence of VTuber viewers. Our research yields several valuable insights, which we then leverage to develop a tool to"recommend"future subscribers to VTubers. By reversing the typical approach of recommending streams to viewers, this tool assists VTubers in pinpointing potential future fans to pay more attention to, and thereby effectively growing their fan community.

Problem

• Virtual_Streamer
• Audience_Engagement
• Fanbase_Expansion

Innovation

• Virtual主播（VTuber）Analytics
• Predictive Modeling
• Fan Engagement Optimization