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.

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.

This dataset presents the number of subscribers on the accounts of the YouTube channels of the entities of Groupe BPCE

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?

Context:

Youtube has introduced automatic generation of subtitles based on speech recognition of uploaded video. This dataset provides collection of subtitles Robert Phoenix The 11th House uploaded podcasts. It serves as database for an introduction to algorithmic analysis of spoken language.

From the podcasts author description: “The Eleventh House is the home of Robert Phoenix, a journalist, blogger, interviewer, astrologer and psychic medium with over 30 years experience in personal readings and coaching, and has been a media personality in TV and radio. The 11th house delves into the supernatural, geopolitics, exopolitics, conspiracy theories, and pop culture.”

Content:

The 11th House speeches dataset consists of 543 subtitles (sets of words) retrieved from Youtube playlists: https://www.youtube.com/user/FreeAssociationRadio/videos

This dataset consists of a single CSV file RobertPhoenixThe11thHouse.csv. The columns are: 'id', 'playlist', 'upload_date', 'title', 'view_count', 'average_rating', 'like_count', 'dislike_count', 'subtitles', which are delimited with a comma.

Text data in columns 'subtitles' is not sentence based, there are not commas or dots. It is only stream of words being translated from speech into text by GoogleVoice (more here https://googleblog.blogspot.com.au/2009/11/automatic-captions-in-youtube.html).

Acknowledgements:

The data was downloaded using youtube-dl package.

Inspiration:

I'm interested in a deeper meaning behind current affairs. (For example see http://www.blogtalkradio.com/freeassociationradio)

A list of the most popular (top 100 by followers) Instagram, Twitter, YouTube, Twitch, and TikTok users. NB! For YouTube the followers are subscribers and the posts are videos.

If using this dataset, please cite the following paper and the current Zenodo repository.

This dataset is described in detail in the following paper:

[1] Yao, Y., Stebner, A., Tuytelaars, T., Geirnaert, S., & Bertrand, A. (2024). Identifying temporal correlations between natural single-shot videos and EEG signals. Journal of Neural Engineering, 21(1), 016018. doi:10.1088/1741-2552/ad2333

The associated code is available at: https://github.com/YYao-42/Identifying-Temporal-Correlations-Between-Natural-Single-shot-Videos-and-EEG-Signals?tab=readme-ov-file

Introduction

The research work leading to this dataset was conducted at the Department of Electrical Engineering (ESAT), KU Leuven.

This dataset contains electroencephalogram (EEG) data collected from 19 young participants with normal or corrected-to-normal eyesight when they were watching a series of carefully selected YouTube videos. The videos were muted to avoid the confounds introduced by audio. For synchronization, a square box was encoded outside of the original frames and flashed every 30 seconds in the top right corner of the screen. A photosensor, detecting the light changes from this flashing box, was affixed to that region using black tape to ensure that the box did not distract participants. The EEG data was recorded using a BioSemi ActiveTwo system at a sample rate of 2048 Hz. Participants wore a 64-channel EEG cap, and 4 electrooculogram (EOG) sensors were positioned around the eyes to track eye movements.

The dataset includes a total of (19 subjects x 63 min + 9 subjects x 24 min) of data. Further details can be found in the following section.

Content

YouTube Videos: Due to copyright constraints, the dataset includes links to the original YouTube videos along with precise timestamps for the segments used in the experiments. The features proposed in 1 have been extracted and can be downloaded here: https://drive.google.com/file/d/1J1tYrxVizrl1xP-W1imvlA_v-DPzZ2Qh/view?usp=sharing.

Raw EEG Data: Organized by subject ID, the dataset contains EEG segments corresponding to the presented videos. Both EEGLAB .set files (containing metadata) and .fdt files (containing raw data) are provided, which can also be read by popular EEG analysis Python packages such as MNE.

The naming convention links each EEG segment to its corresponding video. E.g., the EEG segment 01_eeg corresponds to video 01_Dance_1, 03_eeg corresponds to video 03_Acrob_1, Mr_eeg corresponds to video Mr_Bean, etc.

The raw data have 68 channels. The first 64 channels are EEG data, and the last 4 channels are EOG data. The position coordinates of the standard BioSemi headcaps can be downloaded here: https://www.biosemi.com/download/Cap_coords_all.xls.

Due to minor synchronization ambiguities, different clocks in the PC and EEG recorder, and missing or extra video frames during video playback (rarely occurred), the length of the EEG data may not perfectly match the corresponding video data. The difference, typically within a few milliseconds, can be resolved by truncating the modality with the excess samples.

Signal Quality Information: A supplementary .txt file detailing potential bad channels. Users can opt to create their own criteria for identifying and handling bad channels.

The dataset is divided into two subsets: Single-shot and MrBean, based on the characteristics of the video stimuli.

Single-shot Dataset

The stimuli of this dataset consist of 13 single-shot videos (63 min in total), each depicting a single individual engaging in various activities such as dancing, mime, acrobatics, and magic shows. All the participants watched this video collection.

Video ID Link Start time (s) End time (s)

01_Dance_1 https://youtu.be/uOUVE5rGmhM 8.54 231.20

03_Acrob_1 https://youtu.be/DjihbYg6F2Y 4.24 231.91

04_Magic_1 https://youtu.be/CvzMqIQLiXE 3.68 348.17

05_Dance_2 https://youtu.be/f4DZp0OEkK4 5.05 227.99

06_Mime_2 https://youtu.be/u9wJUTnBdrs 5.79 347.05

07_Acrob_2 https://youtu.be/kRqdxGPLajs 183.61 519.27

08_Magic_2 https://youtu.be/FUv-Q6EgEFI 3.36 270.62

09_Dance_3 https://youtu.be/LXO-jKksQkM 5.61 294.17

12_Magic_3 https://youtu.be/S84AoWdTq3E 1.76 426.36

13_Dance_4 https://youtu.be/0wc60tA1klw 14.28 217.18

14_Mime_3 https://youtu.be/0Ala3ypPM3M 21.87 386.84

15_Dance_5 https://youtu.be/mg6-SnUl0A0 15.14 233.85

16_Mime_6 https://youtu.be/8V7rhAJF6Gc 31.64 388.61

MrBean Dataset

Additionally, 9 participants watched an extra 24-minute clip from the first episode of Mr. Bean, where multiple (moving) objects may exist and interact, and the camera viewpoint may change. The subject IDs and the signal quality files are inherited from the single-shot dataset.

Video ID Link Start time (s) End time (s)

Mr_Bean https://www.youtube.com/watch?v=7Im2I6STbms 39.77 1495.00

Acknowledgement

This research is funded by the Research Foundation - Flanders (FWO) project No G081722N, junior postdoctoral fellowship fundamental research of the FWO (for S. Geirnaert, No. 1242524N), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 802895), the Flemish Government (AI Research Program), and the PDM mandate from KU Leuven (for S. Geirnaert, No PDMT1/22/009).

We also thank the participants for their time and effort in the experiments.

Contact Information

Executive researcher: Yuanyuan Yao, yuanyuan.yao@kuleuven.be

Led by: Prof. Alexander Bertrand, alexander.bertrand@kuleuven.be

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.

This dataset provides estimated YouTube RPM (Revenue Per Mille) ranges for different niches in 2025, based on ad revenue earned per 1,000 monetized views.

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

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.

The research project, SPARTA (Social Media Analysis for Everyone), funded by dtec.bw (which is funded by the European Union – NextGenerationEU), monitors the 2025 German federal election live as it unfolds on TikTok, YouTube and X/Twitter. Since November 7, 2024, the day the "traffic light" coalition collapsed, we have been collecting and analyzing all German-language posts and reposts on X (formerly Twitter) related to the federal elections. Simultaneously, we gather data from TikTok and YouTube, focusing on the accounts of political parties, including those of candidates and current members of the Bundestag, during the same period. Our analysis includes, among other things, the stances expressed towards political parties and leading candidates, the most discussed issues and hashtags, the outreach of political parties across different platforms, the visibility of female candidates, the occurrence of negative campaigning, the rise of toxic language, and the activity of various actors across platforms. We publish the results in real time on our publicly accessible dashboard (https://dtecbw.de/sparta/), which provides interactive and customizable graphics, making it relevant to a broad audience from politics, academia, journalism, and society. To facilitate real-time analysis of the election campaign, we compiled a dataset based on the data of the federal election officer (Bundeswahlleiterin), containing the TikTok, YouTube and X/Twitter handles of all candidates running for a seat in the parliament. This dataset includes the handles as well as additional information about the candidates from eight political parties: AfD, BSW, Buendnis 90/Die Gruenen, CDU, CSU, Die Linke, FDP and SPD.

Concise comparison of the top 10 YouTube alternatives for content creators in 2025. Covers monetization, audience size, and ideal use cases.

ABS-CBN (an initialism of its two predecessors' names, Alto Broadcasting System and Chronicle Broadcasting Network) is a Philippine commercial broadcast network that serves as the flagship property of the ABS-CBN Corporation, a company under Lopez Holdings Corporation owned by the López family. The network is headquartered at the ABS-CBN Broadcasting Center in Quezon City, Philippines.

ABS-CBN is the largest media company in the Philippines and oldest television broadcaster in Southeast Asia.[13]

The network's entertainment YouTube channel is the most-subscribed and most-viewed channel in Southeast Asia, with over 45 million subscribers and over 50 billion views (as of September 2023).[22]

Sample Video

Official YouTube Channel https://www.youtube.com/@abscbnentertainment/

Important Note As you may have noticed, the channel has 220K videos but we only have 655 in this dataset. This is because the API itself doesn't return all the videos as explained in this Stackoverlow post.

Image Generated with Bing Image Generator

License

CC0

Original Data Source: 🇵🇭 ABS-CBN Entertainment YT Channel Comments

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.

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.

This statistic shows a ranking of the estimated number of Youtube users in 2020 in Africa, differentiated by country. The user numbers 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 more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).