Even More Kawaii than Real-Person-Driven VTubers? Understanding How Viewers Perceive AI-Driven VTubers
- URL: http://arxiv.org/abs/2509.20817v1
- Date: Thu, 25 Sep 2025 06:58:13 GMT
- Title: Even More Kawaii than Real-Person-Driven VTubers? Understanding How Viewers Perceive AI-Driven VTubers
- Abstract summary: We investigate viewer perceptions of Neuro-sama, the most popular AI-driven VTuber with 845k followers on Twitch and 753k followers on YouTube.<n>Our findings enhance the understanding of AI-driven VTubers and their impact on digital streaming culture.
- Score: 10.375448790795394
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: VTubers, digital personas represented by animated avatars, have gained massive popularity. Traditionally, VTubers are operated and voiced by human controllers known as Nakanohito. The reliance on Nakanohito, however, poses risks due to potential personal controversies and operational disruptions. The emergence of AI-driven VTubers offers a new model free from these human constraints. While AI-driven VTubers present benefits such as continuous operation and reduced scandal risk, they also raise questions about authenticity and audience engagement. Therefore, to gain deeper insights, we conduct a case study, investigating viewer perceptions of Neuro-sama, the most popular AI-driven VTuber with 845k followers on Twitch and 753k followers on YouTube. We analyze 108k Reddit posts and 136k YouTube comments, aiming to better understand viewer motivations, how AI constructs the virtual persona, and perceptions of the AI as Nakanohito. Our findings enhance the understanding of AI-driven VTubers and their impact on digital streaming culture.
Related papers
- AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games [63.29377274531968]
We introduce the AI GameStore, a scalable and open-ended platform to synthesize new representative human games.<n>We generate 100 such games based on the top charts of Apple App Store and Steam, and evaluate seven frontier vision-language models (VLMs) on short episodes of play.<n>The best models achieved less than 10% of the human average score on the majority of the games, and especially struggled with games that challenge world-model learning, memory and planning.
arXiv Detail & Related papers (2026-02-19T18:17:25Z) - Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning [66.51617619673587]
We present Skyra, a specialized large language model (MLLM) that identifies human-perceivable visual artifacts in AI-generated videos.<n>To support this objective, we construct ViF-CoT-4K for Supervised Fine-Tuning (SFT), which represents the first large-scale AI-generated video dataset with fine-grained human annotations.<n>We then develop a two-stage training strategy that systematically enhances our model's artifact's-temporal perception, explanation capability, and detection accuracy.
arXiv Detail & Related papers (2025-12-17T18:48:26Z) - Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence [31.666988490509237]
We show the pervasiveness and harmful impacts of sycophancy when people seek advice from AI.<n>We find that models are highly sycophantic, affirming users' actions 50% more than humans do.<n>Participants rated sycophantic responses as higher quality, trusted the sycophantic AI model more, and were more willing to use it again.
arXiv Detail & Related papers (2025-10-01T19:26:01Z) - Virtual Stars, Real Fans: Understanding the VTuber Ecosystem [8.461062537658846]
We conduct a comprehensive analysis of VTuber viewers on Bilibili, a leading livestreaming platform where nearly all VTubers in China stream.<n>By compiling a first-of-its-kind dataset covering 2.7M livestreaming sessions, we investigate the characteristics, engagement patterns, and influence of VTuber viewers.<n>We leverage to develop a tool to "recommend" future subscribers to VTubers.
arXiv Detail & Related papers (2025-02-03T17:33:54Z) - The Impacts of AI Avatar Appearance and Disclosure on User Motivation [0.0]
This study examines the influence of perceived AI features on user motivation in virtual interactions.
We conducted a game-based experiment involving over 72,500 participants who solved search problems alone or with an AI companion.
arXiv Detail & Related papers (2024-07-31T10:48:55Z) - Navigating AI Fallibility: Examining People's Reactions and Perceptions of AI after Encountering Personality Misrepresentations [7.256711790264119]
Hyper-personalized AI systems profile people's characteristics to provide personalized recommendations.
These systems are not immune to errors when making inferences about people's most personal traits.
We present two studies to examine how people react and perceive AI after encountering personality misrepresentations.
arXiv Detail & Related papers (2024-05-25T21:27:15Z) - Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes [49.81915942821647]
This paper aims to evaluate the human ability to discern deepfake videos through a subjective study.
We present our findings by comparing human observers to five state-ofthe-art audiovisual deepfake detection models.
We found that all AI models performed better than humans when evaluated on the same 40 videos.
arXiv Detail & Related papers (2024-05-07T07:57:15Z) - Investigating VTubing as a Reconstruction of Streamer Self-Presentation:
Identity, Performance, and Gender [24.535792750960713]
VTubers are live streamers who create streaming content using animated 2D or 3D virtual avatars.
This research explores how this flexibility influences how creators present themselves.
The socio-technical facets of VTubing were found to potentially reduce sexual harassment and sexism, whilst also raising self-objectification concerns.
arXiv Detail & Related papers (2023-07-20T16:53:41Z) - What Do End-Users Really Want? Investigation of Human-Centered XAI for
Mobile Health Apps [69.53730499849023]
We present a user-centered persona concept to evaluate explainable AI (XAI)
Results show that users' demographics and personality, as well as the type of explanation, impact explanation preferences.
Our insights bring an interactive, human-centered XAI closer to practical application.
arXiv Detail & Related papers (2022-10-07T12:51:27Z) - The Who in XAI: How AI Background Shapes Perceptions of AI Explanations [61.49776160925216]
We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations.
We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design.
arXiv Detail & Related papers (2021-07-28T17:32:04Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - The Threat of Offensive AI to Organizations [52.011307264694665]
This survey explores the threat of offensive AI on organizations.
First, we discuss how AI changes the adversary's methods, strategies, goals, and overall attack model.
Then, through a literature review, we identify 33 offensive AI capabilities which adversaries can use to enhance their attacks.
arXiv Detail & Related papers (2021-06-30T01:03:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.