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.

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.

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.

Description

This Dataset contains details of Top 1000 most subscribed YouTube channels in Germany as of 13th Feb 2025.

Attribute Information

• Rank : The rank of the videos.
• Youtuber: Name of the channel.
• Subscribers: No of Subscribers
• Video Views : No of views for the videos.
  • Video Count: No of the videos.
• Category : Category of the videos.
• Started: Year on which the channel Started.

Acknowledgements

https://us.youtubers.me/

The number of Youtube users in India was forecast to continuously increase between 2024 and 2029 by in total 222.2 million users (+34.88 percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach 859.26 million 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 Sri Lanka and Nepal.

Dataset

This dataset was created by Ahmed El-Affendi

Released under CC BY-SA 4.0

Contents

Context

Who are leading Pakistan on YouTube? Here is the list of Top 250 channels based on their subscribers count

Content

The dataset contains Top 250 ranking of YouTube Channels in Pakistan along with Channel's name and subscribers count

Inspiration

Let's find out what Pakistan is watching?

The YouTube-100M data set consists of 100 million YouTube videos: 70M training videos, 10M evaluation videos, and 20M validation videos. Videos average 4.6 minutes each for a total of 5.4M training hours. Each of these videos is labeled with 1 or more topic identifiers from a set of 30,871 labels. There are an average of around 5 labels per video. The labels are assigned automatically based on a combination of metadata (title, description, comments, etc.), context, and image content for each video. The labels apply to the entire video and range from very generic (e.g. “Song”) to very specific (e.g. “Cormorant”). Being machine generated, the labels are not 100% accurate and of the 30K labels, some are clearly acoustically relevant (“Trumpet”) and others are less so (“Web Page”). Videos often bear annotations with multiple degrees of specificity. For example, videos labeled with “Trumpet” are often labeled “Entertainment” as well, although no hierarchy is enforced.

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Ffcfb9276216141c05dc1d6bb8157c97b%2Fchef.png?generation=1707199494679127&alt=media" alt="">

I've collected information on the published videos, along with the threads and comments of well-known YouTube Cooking Channels.

https://www.youtube.com/watch?v=wUbi0p-Nw2A" alt="">

Zach King, FaZe Rug & Sydney Morgan Attempt to Make Sandwiches for Gordon Ramsay | Idiot Sandwich

Channels

• Pro Home Cooks
• Jamie Oliver
• Gordon Ramsay
• Honeysuckle
• You Suck At Cooking
• Sorted Food
• J. Kenji López-Alt
• Babish Culinary Universe
• Joshua Weissman
• Ethan Chlebowski
• Tasty
• 美食作家王刚
• samseats

Important Note

There may be some missing videos esp if the channel has more than 500+ videos, this is because the API itself doesn't return all the videos as explained in this Stackoverlow post

The dataset is first introduced in the following paper: Siqi Wu, Marian-Andrei Rizoiu, and Lexing Xie. Beyond Views: Measuring and Predicting Engagement in Online Videos. In AAAI International Conference on Weblogs and Social Media (ICWSM), 2018. Tweeted videos dataset This dataset contains YouTube videos published between July 1st and August 31st, 2016. To be collected, the video needs (a) be mentioned on Twitter during aforementioned collection period; (b) have insight statistics available; (c) have at least 100 views within the first 30 days after upload. Quality videos datasets These datasets contain videos deemed of high quality by domain experts. Vevo videos: Videos of verified Vevo artists, as of August 31st, 2016. Billboard16 videos: Videos of 2016 Billboard Hot 100 chart. Top news videos: Videos of top 100 most viewed News channels. freebase_mid_type_name.csv It maps a freebase mid to a real-world entity. See more details in this data description.

YouTube Ads Revenue Dataset contains revenue generated from YouTube ads in millions of dollars. It contains revenue-generated data on a quarterly period.

Alphabet parent company of Google, owned YouTube has a financial year from Jan to Dec. So, so we have quarterly reports to collect their revenue data

Date: End of quarter.

Image by Sly from Pixabay

Our dataset offers a unique blend of attributes from YouTube and Google Maps, empowering users with comprehensive insights into online content and geographical reach. Let's delve into what makes our data stand out:

Unique Attributes: - From YouTube: Detailed video information including title, description, upload date, video ID, and channel URL. Video metrics such as views, likes, comments, and duration are also provided. - Creator Info: Access author details like name and channel URL. - Channel Information: Gain insights into channel title, description, location, join date, and visual branding elements like logo and banner URLs. - Channel Metrics: Understand a channel's performance with metrics like total views, subscribers, and video count. - Google Maps Integration: Explore business ratings from Google My Business and location data from Google Maps.

Data Sourcing: - Our data is meticulously sourced from publicly available information on YouTube and Google Maps, ensuring accuracy and reliability.

Primary Use-Cases: - Marketing: Analyze video performance metrics to optimize content strategies. - Research: Explore trends in creator behavior and audience engagement. - Location-Based Insights: Utilize Google Maps data for market research, competitor analysis, and location-based targeting.

Fit within Broader Offering: - This dataset complements our broader data offering by providing rich insights into online content consumption and geographical presence. It enhances decision-making processes across various industries, including marketing, advertising, research, and business intelligence.

Usage Examples: - Marketers can identify popular video topics and optimize advertising campaigns accordingly. - Researchers can analyze audience engagement patterns to understand viewer preferences. - Businesses can assess their Google My Business ratings and geographical distribution for strategic planning.

With scalable solutions and high-quality data, our dataset offers unparalleled depth for extracting actionable insights and driving informed decisions in the digital landscape.

Overview

Welcome to the Music Informatics for Radio Across the GlobE (MIRAGE) MetaCorpus. The current (v0.2) development release consists of metadata (e.g., artist name, track title) and musicological features (e.g., instrument list, voice type, tempo) for 1 million events streaming on 10,000 internet radio stations across the globe, with 100 events from each station.

Users who wish to access, interact with, and/or export metadata from the MIRAGE-MetaCorpus may also visit the MIRAGE online dashboard at the following url:

• https://pearl-laboratory.github.io/mirage-mc/

Attribution

The current MIRAGE-MetaCorpus is available under a CC4 license. Users may cite the dataset here:

Sears, David R.W. “Music Informatics for Radio Across the Globe (MIRAGE) Metacorpus -- 2024”. Zenodo, July 19, 2024. https://doi.org/10.5281/zenodo.12786202.

Users accessing the MIRAGE-MetaCorpus using the online dashboard should also cite the following ISMIR paper:

Ngan V.T. Nguyen, Elizabeth A.M. Acosta, Tommy Dang, and David R.W. Sears. "Exploring Internet Radio Across the Globe with the MIRAGE Online Dashboard," in Proceedings of the 25th International Society for Music Information Retrieval Conference (San Francisco, CA, 2024).

Data Sources

This repository of the MIRAGE-MetaCorpus contains 81 metadata variables from the following open-access sources:

• Radio Garden (RG) -- https://radio.garden
• Natural Earth map data set (NE) -- https://www.naturalearthdata.com/
• Internet Radio Station Stream Encoder (SE)
• Annotator Review (AR)
• Monitoring/Matching Algorithm (MA)
• WikiData (WD) -- https://www.wikidata.org
• MusicBrainz (MB) -- https://musicbrainz.org/

Each event also includes attribution metadata from the following commercial sources:

• Spotify (SP) -- https://open.spotify.com/
  • Note that users may examine an additional 19 metadata variables on the MIRAGE online dashboard that were obtained from the Spotify API.
• Musixmatch (MX) -- https://www.musixmatch.com/
• YouTube (YT) -- https://www.youtube.com/
• Genius (GE) -- https://genius.com/
• AZlyrics (AZ) -- https://www.azlyrics.com/

Data Sets

The metadata reflect information about each event's location (e.g., city, country), station (name, format, url), event (id, local time at station, etc.), artist (name, voice type, etc.), and track (e.g., title, year of release, etc.). For that reason, the MIRAGE-MetaCorpus includes the following datasets:

• MIRAGE.csv -- the complete metacorpus (1 million)
• events.csv -- all event-level metadata (1 million)
• tracks.csv -- all track-level metadata (414,886)
• artists.csv -- all artist-level metadata (259,783)
• stations.csv -- all station-level metadata (10,000)
• locations.csv -- all location-level metadata (4,324)

A subset of the MIRAGE-MetaCorpus is also available for events with metadata from online music libraries that reliably matched the event's description in the radio station's stream encoder:

• MIRAGE_reliable.csv (473,850)
• events_reliable.csv (473,850)
• tracks_reliable.csv (204,969)
• artists_reliable.csv (80,005)
• stations_reliable.csv (9,284)
• locations_reliable.csv (4,142)

Contact

If you are a copyright owner for any of the metadata that appears in the MIRAGE-MetaCorpus and would like us to remove your metadata, please contact the developer team at the following email address: miragedashboard@gmail.com

Hi, welcome to the most versatile dataset of YouTube thumbnails. This dataset goes hand in hand with the YouTubers-Sayting-things dataset. The same set of video ids was used to generate this dataset, and I will be updating both at once.

Now, obviously not all videos have a set thumbnail. In those cases, YouTube assigns a random frame as the subtitle, Only for those video Ids, we don't have the image.

Genres and channels

This new worldwide dataset evaluated the quality and reliability of web-based health information regarding tinnitus sound therapy for patient education. The popularity and usage of tinnitus sound therapy have greatly increased online due to the proliferation of smart devices, though it remains unclear whether this is an effective method for patient education or assisting patients with tinnitus. If low-quality videos were to garner greater patient attention, concerns would arise over the veracity of clinical practices and treatment efficacy.Therefore, YouTube was chosen in this dataset as a prime representation of online sharing platforms and a sample of 100 of the most easily accessible videos was selected through evaluative measures.This dataset presents the precise rankings of the 100 videos on a range of questionnaires, including DISCERN, GQS, JAMA-benchmark, PEMAT, etc., and is meticulously categorised. This information was primarily utilised to evaluate different aspects of the quality of tinnitus sound therapy videos accessible online. It is anticipated that these results will be valuable for future meta-analyses of health-related information evaluations on the internet.

Motivation

The rise of online media has enabled users to choose various unethical and artificial ways of gaining social growth to boost their credibility (number of followers/retweets/views/likes/subscriptions) within a short time period. In this work, we present ABOME, a novel data repository consisting of datasets collected from multiple platforms for the analysis of blackmarket-driven collusive activities, which are prevalent but often unnoticed in online media. ABOME contains data related to tweets and users on Twitter, YouTube videos, YouTube channels. We believe ABOME is a unique data repository that one can leverage to identify and analyze blackmarket based temporal fraudulent activities in online media as well as the network dynamics.

License

Creative Commons License.

Description of the dataset

- Historical Data

We collected the metadata of each entity present in the historical data

Twitter:

We collected the following fields for retweets and followers on Twitter:

user_details: A JSON object representing a Twitter user.

tweet_details: A JSON object representing a tweet.

tweet_retweets: A JSON list of tweet objects representing the most recent 100 retweets of a given tweet.

1. https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object↩︎

2. https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object↩︎

YouTube:

We collected the following fields for YouTube likes and comments:

is_family_friendly: Whether the video is marked as family friendly or not.

genre: Genre of the video.

duration: Duration of the video in ISO 8601 format (duration type). This format is generally used when the duration denotes the amount of intervening time in a time interval.

description: Description of the video.

upload_date: Date that the video was uploaded.

is_paid: Whether the video is paid or not.

is_unlisted: The privacy status of the video, i.e., whether the video is unlisted or not. Here, the flag unlisted indicates that the video can only be accessed by people who have a direct link to it.

statistics: A JSON object containing the number of dislikes, views and likes for the video.

comments: A list of comments for the video. Each element in the list is a JSON object of the text (the comment text) and time (the time when the comment was posted).

We collected the following fields for YouTube channels:

channel_description: Description of the channel.

hidden_subscriber_count: Total number of hidden subscribers of the channel.

published_at: Time when the channel was created. The time is specified in ISO 8601 format (YYYY-MM-DDThh:mm:ss.sZ).

video_count: Total number of videos uploaded to the channel.

subscriber_count: Total number of subscribers of the channel.

view_count: The number of times the channel has been viewed.

kind: The API resource type (e.g., youtube#channel for YouTube channels).

country: The country the channel is associated with.

comment_count: Total number of comments the channel has received.

etag: The ETag of the channel which is an HTTP header used for web browser cache validation.

The historical data is stored in five directories named according to the type of data inside it. Each directory contains json files corresponding to the data described above.

- Time-series Data

We collect the following time-series data for retweets and followers on Twitter:

user_timeline: This is a JSON list of tweet objects in the user’s timeline, which consists of the tweets posted, retweeted and quoted by the user. The file created at each time interval contains the new tweets posted by the user during each time interval.

user_followers: This is a JSON file containing the user ids of all the followers of a user that were added or removed from the follower list during each time interval.

user_followees: This is a JSON file consisting of the user ids of all the users followed by a user, i.e., the followees of a user, that were added or removed from the followee list during each time interval.

tweet_details: This is a JSON object representing a given tweet, collected after every time interval.

tweet_retweets: This is a JSON list of tweet objects representing the most recent 100 retweets of a given tweet, collected after every time interval.

The time-series data is stored in directories named according to the timestamp of the collection time. Each directory contains sub-directories corresponding to the data described above.

Data Anonymization

The data is anonymized by removing all Personally Identifiable Information (PII) and generating pseud-IDs corresponding to the original IDs. A consistent mapping between the original and pseudo-IDs is maintained to maintain the integrity of the data.

The legal deposit of the audiovisual web is the part of the legal deposit which the legislator has entrusted to the National Audiovisual Institute (Law No 2006-961 of 1 August 2006 on copyright and related rights in the information society, known as the “DADVSI Law”). It started in February 2009 with the collection of websites (audiovisual media services and online public communication services) and since 2014 includes social networks (including Twitter accounts) linked to French audiovisual.

As part of the creation of audiovisual thematic corpuses by Ina documentalists, a study on the online presence of reality TV shows was conducted between November 2020 and January 2021.

For each identified reality show as well as for its participants and production companies, social media accounts, Youtube channels, Wikipedia pages, websites and hashtags have been identified.

The dataset contains the completeness of these web objects and when available, documentary information and metrics (number of subscribers, number of subscriptions, etc.) as of January 2021. All the publications, videos associated with these accounts and the websites can only be consulted in the hands of the INA, the custodian of this legal deposit.

The dataset is divided into 4 data sub-games: — “Depot_legal_du_web-_telealité-_emissions.csv” contains all web objects by reality show — “Depot_legal_du_web-_teleality-_participant.e.s.csv” contains all the web objects per participant — “Depot_legal_du_web-_teleality-_production.csv” contains all web objects per production company — “Depot_legal_du_web-_teleality-_dones_documentaires.csv” contains all documentary data and metrics by web objects — “Depot_legal_du_web-_teleality-_dones_documentary_dictionnaire.csv” contains a dictionary of the previous file.

The re-use of personal data present in the data sets published by Ina constitutes the processing of personal data as defined by Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 known as the General Data Protection Regulation (the “GDPR”) and Law No 78-17 of 6 January 1978 on computing, files and freedoms as amended, together ‘the Data Protection Regulation’. The re-user is therefore subject to compliance with the legal framework resulting from the Data Protection Regulation in order to ensure that such re-use of personal data is lawful. In any event, Ina disclaims any liability for non-compliance by a re-user with the above-mentioned rules.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

The legal deposit of the audiovisual web is the part of the legal deposit which the legislator has entrusted to the National Audiovisual Institute (Law No 2006-961 of 1 August 2006 on copyright and related rights in the information society, known as the “DADVSI Law”). It started in February 2009 with the collection of websites (audiovisual media services and online public communication services) and since 2014 includes social networks (including Twitter accounts) linked to French audiovisual. As part of the creation of audiovisual thematic corpuses by Ina documentalists, a study on the online presence of reality TV shows was conducted between November 2020 and January 2021. For each identified reality show as well as for its participants and production companies, social media accounts, Youtube channels, Wikipedia pages, websites and hashtags have been identified. The dataset contains the completeness of these web objects and when available, documentary information and metrics (number of subscribers, number of subscriptions, etc.) as of January 2021. All the publications, videos associated with these accounts and the websites can only be consulted in the hands of the INA, the custodian of this legal deposit. The dataset is divided into 4 data sub-games: — “Depot_legal_du_web-_telealité-_emissions.csv” contains all web objects by reality show — “Depot_legal_du_web-_teleality-_participant.e.s.csv” contains all the web objects per participant — “Depot_legal_du_web-_teleality-_production.csv” contains all web objects per production company — “Depot_legal_du_web-_teleality-_dones_documentaires.csv” contains all documentary data and metrics by web objects — “Depot_legal_du_web-_teleality-_dones_documentary_dictionnaire.csv” contains a dictionary of the previous file. The re-use of personal data present in the data sets published by Ina constitutes the processing of personal data as defined by Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 known as the General Data Protection Regulation (the “GDPR”) and Law No 78-17 of 6 January 1978 on computing, files and freedoms as amended, together ‘the Data Protection Regulation’. The re-user is therefore subject to compliance with the legal framework resulting from the Data Protection Regulation in order to ensure that such re-use of personal data is lawful. In any event, Ina disclaims any liability for non-compliance by a re-user with the above-mentioned rules.

Through its Employment and Financial Services (EFS) division, Seniors, Community and Social Services’ (SCSS) programs form a strong foundation of support to help many Albertans find and keep jobs. The ministry provides financial support, employment services, career resources, referrals, information on job fairs and workshops, and local labor market information. The goal is to help individuals and families gain independence by providing opportunities to enhance their skills to get jobs. The alis.alberta.ca website provides employment resources to help Albertans enhance their employability, plan for education and training, make informed career choices, and connect to and be successful in the labour market. This dataset provides information on web traffic statistics for the alis website, including information on pageviews and web sessions, demographic information for web sessions, and traffic information for the alis YouTube channel (https://www.youtube.com/user/ALISwebsite).