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Use our YouTube profiles dataset to extract both business and non-business information from public channels and filter by channel name, views, creation date, or subscribers. Datapoints include URL, handle, banner image, profile image, name, subscribers, description, video count, create date, views, details, and more. You may purchase the entire dataset or a customized subset, depending on your needs. Popular use cases for this dataset include sentiment analysis, brand monitoring, influencer marketing, and more.
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🇬🇧 English:
This synthetic dataset is designed for predicting the popularity of YouTube videos using metadata. It includes fields like video title, duration, tags, and view count. Useful for regression modeling, feature engineering, and exploring social media analytics.
Use this dataset to:
Features:
🇹🇷 Türkçe:
Bu sentetik veri seti, YouTube videolarının popülerliğini (izlenme sayısını) tahmin etmek amacıyla oluşturulmuştur. Başlık uzunluğu, etiket sayısı ve video süresi gibi meta verileri içermektedir. Sosyal medya analizi ve regresyon modeli geliştirmek isteyenler için uygundur.
Bu veri seti sayesinde:
Değişkenler:
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Use our YouTube Videos dataset to extract detailed information from public videos and filter by video title, views, upload date, or likes. Data points include video URL, title, description, thumbnail, upload date, view count, like count, comment count, tags, and more. You can purchase the entire dataset or a customized subset, tailored to your needs. Popular use cases for this dataset include trend analysis, content performance tracking, brand monitoring, and influencer campaign optimization.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
YouTube maintains a list of the top trending videos on the platform. According to Variety magazine, “To determine the year’s top-trending videos, YouTube uses a combination of factors including measuring users interactions (number of views, shares, comments and likes). Note that they’re not the most-viewed videos overall for the calendar year”.
Note that this dataset is a structurally improved version of this dataset.
This dataset includes several months (and counting) of data on daily trending YouTube videos. Data is included for the IN, US, GB, DE, CA, FR, RU, BR, MX, KR, and JP regions (India, USA, Great Britain, Germany, Canada, France, Russia, Brazil, Mexico, South Korea, and, Japan respectively), with up to 200 listed trending videos per day.
Each region’s data is in a separate file. Data includes the video title, channel title, publish time, tags, views, likes and dislikes, description, and comment count.
The data also includes a category_id field, which varies between regions. To retrieve the categories for a specific video, find it in the associated JSON. One such file is included for each of the 11 regions in the dataset.
For more information on specific columns in the dataset refer to the column metadata.
This dataset was collected using the YouTube API. This dataset is the updated version of Trending YouTube Video Statistics.
Possible uses for this dataset could include: - Sentiment analysis in a variety of forms - Categorizing YouTube videos based on their comments and statistics. - Training ML algorithms like RNNs to generate their own YouTube comments. - Analyzing what factors affect how popular a YouTube video will be. - Statistical analysis over time.
For further inspiration, see the kernels on this dataset!
The global number of Youtube users in was forecast to continuously increase between 2024 and 2029 by in total 232.5 million users (+24.91 percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach 1.2 billion users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Youtube users in countries like Africa and South America.
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Over the past few years YouTube has became a popular site for video broadcasting and earning money by publishing various different skills in the form of videos. For some people it has become a main source to earn money. Getting the videos trending among the viewers is one of the major tasks which each and every content creator wants. Popularity of any video and its reach to the audience is completely based on YouTube's Recommendation algorithm. This document is a dataset descriptor for the dataset collected over the time span of about 45 days during the Israel-Hamas War
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Associative Tag Recommendation Exploiting Multiple Textual FeaturesFabiano Belem, Eder Martins, Jussara M. Almeida Marcos Goncalves In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, July. 2011AbstractThis work addresses the task of recommending relevant tags to a target object by jointly exploiting three dimen- sions of the problem: (i) term co-occurrence with tags preassigned to the target object, (ii) terms extracted from mul- tiple textual features, and (iii) several metrics of tag relevance. In particular, we propose several new heuristic meth- ods, which extend previous, highly effective and efficient, state-of-the-art strategies by including new metrics that try to capture how accurately a candidate term describes the object’s content. We also exploit two learning to rank techniques, namely RankSVM and Genetic Programming, for the task of generating ranking functions that combine multiple metrics to accurately estimate the relevance of a tag to a given object. We evaluate all proposed methods in various scenarios for three popular Web 2.0 applications, namely, LastFM, YouTube and YahooVideo. We found that our new heuristics greatly outperform the methods on which they are based, producing gains in precision of up to 181%, as well as another state-of-the-art technique, with improvements in precision of up to 40% over the best baseline in any scenario. Some further improvements can also be achieved, in some scenarios, with the new learning-to-rank based strategies, which have the additional advantage of being quite flexible and easily extensible to exploit other aspects of the tag recommendation problem.Bibtex Citation@inproceedings{belem@sigir11, author = {Fabiano Bel\'em and Eder Martins and Jussara Almeida and Marcos Gon\c{c}alves}, title = {Associative Tag Recommendation Exploiting Multiple Textual Features}, booktitle = {{Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR'11)}}, month = {{July}}, year = {2011} }
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YouTube keeps track of the most popular videos that are being seen on the site. Several months' worth of daily trending YouTube video statistics are included in this data set. Data for France and the USA are included. The videos on this list are those that users have liked and have received the most views, comments, and likes from other users. These videos are then displayed on the trending page. The greatest videos are shown at the top of the page by ranking these videos according to a ratio of views, likes, comments, and shares.
This dataset is a daily record of the top trending YouTube videos.
content: Data about daily trending YouTube videos for several months, and counting, is included in this dataset. Up to 200 trending videos are published each day, with data for the US and FR regions (the USA and France, respectively) included.
As of February 2025, India was the country with the largest YouTube audience by far, with approximately 491 million users engaging with the popular social video platform. The United States followed, with around 253 million YouTube viewers. Brazil came in third, with 144 million users watching content on YouTube. The United Kingdom saw around 54.8 million internet users engaging with the platform in the examined period. What country has the highest percentage of YouTube users? In July 2024, the United Arab Emirates was the country with the highest YouTube penetration worldwide, as around 94 percent of the country's digital population engaged with the service. In 2024, YouTube counted around 100 million paid subscribers for its YouTube Music and YouTube Premium services. YouTube mobile markets In 2024, YouTube was among the most popular social media platforms worldwide. In terms of revenues, the YouTube app generated approximately 28 million U.S. dollars in revenues in the United States in January 2024, as well as 19 million U.S. dollars in Japan.
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Context: The data was created to build like a analysis which can use to be project for a fresher.
Source: The csv data was scraped from 'https://www.youtube.com/'. You can find the codes from 'https://github.com/riju1999'
Inspiration: You can build a website for your friends using the csv data.
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We publish a data set for YouTube's mobile streaming client, which follows the popular Dynamic Adaptive Streaming over HTTP (DASH) standard. The data was measured over 4 months, at 2 separate locations in Europe, at the network, transport and application layer for DASH.
VideoLQ consists of videos downloaded from various video hosting sites such as Flickr and YouTube, with a Creative Common license.
Creative Commons YouTube
Description
YouTube is a large-scale video-sharing platform where users have the option of uploading content under a CC BY license. To collect high-quality speech-based textual content and combat the rampant license laundering on YouTube, we manually curated a set of over 2,000 YouTube channels that consistently release original openly licensed content containing speech. The resulting collection spans a wide range of genres, including lectures… See the full description on the dataset page: https://huggingface.co/datasets/common-pile/youtube_filtered.
The proposed Extended-YouTube Faces (E-YTF) is an extension of the famous YouTube Faces (YTF) dataset and is specifically designed to further push the challenges of face recognition by addressing the problem of open-set face identification from heterogeneous data i.e. still images vs video.
As of June 2022, more than *** hours of video were uploaded to YouTube every minute. This equates to approximately ****** hours of newly uploaded content per hour. The amount of content on YouTube has increased dramatically as consumer’s appetites for online video has grown. In fact, the number of video content hours uploaded every 60 seconds grew by around ** percent between 2014 and 2020. YouTube global users Online video is one of the most popular digital activities worldwide, with ** percent of internet users worldwide watching more than ** hours of online videos on a weekly basis in 2023. It was estimated that in 2023 YouTube would reach approximately *** million users worldwide. In 2022, the video platform was one of the leading media and entertainment brands worldwide, with a value of more than ** billion U.S. dollars. YouTube video content consumption The most viewed YouTube channels of all time have racked up billions of viewers, millions of subscribers and cover a wide variety of topics ranging from music to cosmetics. The YouTube channel owner with the most video views is Indian music label T-Series, which counted ****** billion lifetime views. Other popular YouTubers are gaming personalities such as PewDiePie, DanTDM and Markiplier.
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Description
This Dataset contains details of Most popular 1000 youtube videos as of 27th Jan 2025.
Attribute Information
Acknowledgements
EDUVSUM contains educational videos with subtitles from three popular e-learning platforms: Edx,YouTube, and TIB AV-Portal that cover the following topics: crash course on history of science and engineering, computer science, python and web programming, machine learning and computer vision, Internet of things (IoT), and software engineering. In total, the current version of the dataset contains 98 videos with ground truth values annotated by a user with an academic background in computer science.
As of February 2025, ** percent of the YouTube global audience was composed of male users aged between 25 and 34 years, as well as around *** percent of female users of the same age. Male users aged between 35 and 44 years on the platform accounted for **** percent of the total, while women of the same age using YouTube had an audience share of *** percent in the examined period. YouTube’s global popularity The number of monthly active users on YouTube reached almost *** billion in April 2024, making it the second most popular social network on the internet. The platform's popularity spans all over the world, with India and the United States having the largest YouTube audiences. As of April 2024, the audience of YouTube in India was around *** million, while the United States recorded a YouTube audience of around *** million users.
YouTube’s digital revenues One of YouTube's leading monetization methods include advertising, with the company generating around **** billion U.S. dollars in the first quarter of 2024. Additionally, the platform generated over ** million dollars in the United States through in-app purchases, as well as over **** million U.S. dollars in revenues from mobile app users in Japan.
Sexually Abusive Comments and specific words collection from popular youtube videos such as music videos and cartoons (Peppa Pig)
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Unlock powerful insights with our custom music datasets, offering access to millions of records from popular music platforms like Spotify, SoundCloud, Amazon Music, YouTube Music, and more. These datasets provide comprehensive data points such as track titles, artists, albums, genres, release dates, play counts, playlist details, popularity scores, user-generated tags, and much more, allowing you to analyze music trends, listener behavior, and industry patterns with precision. Use these datasets to optimize your music strategies by identifying trending tracks, analyzing artist performance, understanding playlist dynamics, and tracking audience preferences across platforms. Gain valuable insights into streaming habits, regional popularity, and emerging genres to make data-driven decisions that enhance your marketing campaigns, content creation, and audience engagement. Whether you’re a music producer, marketer, data analyst, or researcher, our music datasets empower you with the data needed to stay ahead in the ever-evolving music industry. Available in various formats such as JSON, CSV, and Parquet, and delivered via flexible options like API, S3, or email, these datasets ensure seamless integration into your workflows.
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Use our YouTube profiles dataset to extract both business and non-business information from public channels and filter by channel name, views, creation date, or subscribers. Datapoints include URL, handle, banner image, profile image, name, subscribers, description, video count, create date, views, details, and more. You may purchase the entire dataset or a customized subset, depending on your needs. Popular use cases for this dataset include sentiment analysis, brand monitoring, influencer marketing, and more.