Best Youtube Datasets to use in 2025
YouTube datasets refer to collections of structured data that have been extracted from the YouTube platform. These datasets typically include information such as video metadata (title, description, tags), user engagement metrics (views, likes, comments), and demographic data (age, gender, location) of the viewers. YouTube datasets are valuable for various applications, including market research, content analysis, recommendation systems, and trend analysis. By leveraging these datasets, businesses and researchers can gain insights into user behavior, content popularity, and audience preferences on the YouTube platform.
Social Media Data | Linkedin, Youtube, TwitterX | Global Coverage | 120M+ Contacts | (Verified E-mail, Direct Dails) | Live Profile Links
Webautomation Social Media Data | 10M+ YouTube Creators User Profile Dataset | 10M+ Influencers | Web-Scraped | GDPR compliant
Influencer Database 2025 | TikTok and Instagram Influencers Database | 10M+ Influencer Data
YouTube & Google Maps Data | 21+ Attributes | Channel metrics, Creator Info, Video Metrics | Google My Business Rating, Maps | Social Media Data
Social Media Data - B2B Social Media Data ,B2B Social links, Scrape all publicly available links and profiles | 50% Cost Saving | Free Sample
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37,500 Technographics covering 38 Million Companies -RampedUp
OutreachGenius' B2B marketing data | with social media links for over 3M+ companies | across more than 180+ countries
OutreachGenius' B2B Maketing Data | Verified Company Contact | 8M+ with Phone & Email | Scraped Directly from Websites in Last 30 Days for Accuracy
App + Web Consumer Data | MFour's 1st Party - App + Web Usage Data | 2M consumers, 3B+ events verified, US consumers | CCPA Compliant
Outscraper Google Maps Scraper
What are YouTube datasets?
YouTube datasets refer to collections of structured data that have been extracted from the YouTube platform. These datasets typically include information such as video metadata (title, description, tags), user engagement metrics (views, likes, comments), and demographic data (age, gender, location) of the viewers.
What can YouTube datasets be used for?
YouTube datasets are valuable for various applications, including market research, content analysis, recommendation systems, and trend analysis. By leveraging these datasets, businesses and researchers can gain insights into user behavior, content popularity, and audience preferences on the YouTube platform.
How can businesses benefit from YouTube datasets?
Businesses can benefit from YouTube datasets by analyzing user engagement metrics to understand the popularity of their own videos or competitor videos. They can also use demographic data to target specific audience segments and optimize their marketing strategies. Additionally, YouTube datasets can help businesses identify trends and patterns in user behavior, allowing them to make data-driven decisions for content creation and advertising campaigns.
How can researchers benefit from YouTube datasets?
Researchers can use YouTube datasets to study various aspects of user behavior and content consumption patterns. They can analyze video metadata to understand the characteristics of popular videos and identify factors that contribute to their success. Researchers can also explore the relationship between user engagement metrics and demographic data to gain insights into audience preferences and trends.
Where can I find YouTube datasets?
YouTube datasets can be found on various platforms and repositories, such as academic research databases, data sharing platforms, and open data initiatives. Some examples include the YouTube Data API, Kaggle, and the Internet Archive. Additionally, researchers and organizations may create and share their own YouTube datasets for specific research projects.
Are YouTube datasets freely available?
The availability of YouTube datasets varies depending on the source. Some datasets may be freely available for public use, while others may require permission or have certain usage restrictions. It is important to review the terms and conditions of each dataset to understand any limitations or licensing requirements before using the data.