YTLive: A Dataset of Real-World YouTube Live Streaming Sessions

2025-10-24

AI Summary

Live streaming research has long been hindered by the absence of large-scale, high-temporal-resolution public datasets capturing real-world viewer behavior. To address this gap, we introduce the first large-scale, real-world live streaming dataset derived from the YouTube Researcher Program—comprising 12,156 live streams and 507,000 five-minute granularity concurrent viewer counts. Leveraging YouTube’s official API for data acquisition and applying time-series modeling techniques, we systematically quantify the relationships among stream duration, broadcast time, and audience dynamics. Our analysis reveals that weekend afternoon streams attract larger and more stable audiences; short streams exhibit sharply peaked, highly concentrated traffic patterns, whereas long streams show slower growth and significantly higher volatility. This dataset fills a critical void in the field and establishes a reproducible, empirically grounded benchmark for research in adaptive bitrate control, quality-of-experience (QoE) modeling, and real-time recommendation systems.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Data Stream Mining
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Live streaming plays a major role in today's digital platforms, supporting entertainment, education, social media, etc. However, research in this field is limited by the lack of large, publicly available datasets that capture real-time viewer behavior at scale. To address this gap, we introduce YTLive, a public dataset focused on YouTube Live. Collected through the YouTube Researcher Program over May and June 2024, YTLive includes more than 507000 records from 12156 live streams, tracking concurrent viewer counts at five-minute intervals along with precise broadcast durations. We describe the dataset design and collection process and present an initial analysis of temporal viewing patterns. Results show that viewer counts are higher and more stable on weekends, especially during afternoon hours. Shorter streams attract larger and more consistent audiences, while longer streams tend to grow slowly and exhibit greater variability. These insights have direct implications for adaptive streaming, resource allocation, and Quality of Experience (QoE) modeling. YTLive offers a timely, open resource to support reproducible research and system-level innovation in live streaming. The dataset is publicly available at github.

Problem

• Lack of large public datasets for live streaming research
• Capturing real-time viewer behavior patterns at scale
• Understanding temporal viewing dynamics and audience engagement

Innovation

• Collected YouTube Live data via Researcher Program
• Tracked concurrent viewers at five-minute intervals
• Analyzed temporal patterns for adaptive streaming optimization