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.

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.

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.

This is the statistics for the Top 10 songs of various spotify artists and their YouTube videos. The Creators above generated the data and uploaded it to Kaggle on February 6-7 2023. The license to use this data is "CC0: Public Domain", allowing the data to be copied, modified, distributed, and worked on without having to ask permission. The data is in numerical and textual CSV format as attached. This dataset contains the statistics and attributes of the top 10 songs of various artists in the world. As described by the creators above, it includes 26 variables for each of the songs collected from spotify. These variables are briefly described next:

Track: name of the song, as visible on the Spotify platform. Artist: name of the artist. Url_spotify: the Url of the artist. Album: the album in wich the song is contained on Spotify. Album_type: indicates if the song is relesead on Spotify as a single or contained in an album. Uri: a spotify link used to find the song through the API. Danceability: describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable. Energy: is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy. Key: the key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1. Loudness: the overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db. Speechiness: detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks. Acousticness: a confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic. Instrumentalness: predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly "vocal". The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0. Liveness: detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live. Valence: a measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry). Tempo: the overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration. Duration_ms: the duration of the track in milliseconds. Stream: number of streams of the song on Spotify. Url_youtube: url of the video linked to the song on Youtube, if it have any. Title: title of the videoclip on youtube. Channel: name of the channel that have published the video. Views: number of views. Likes: number of likes. Comments: number of comments. Description: description of the video on Youtube. Licensed: Indicates whether the video represents licensed content, which means that the content was uploaded to a channel linked to a YouTube content partner and then claimed by that partner. official_video: boolean value that indicates if the video found is the official video of the song. The data was last updated on February 7, 2023.

In the third quarter of 2020, it was found that 77 percent of U.S. internet users aged 15 to 25 years accessed YouTube. YouTube in the United States With over 126 million unique monthly viewers, YouTube is by far the most popular online video property in the United States. The platform’s mobile presence is also significant, as YouTube consistently ranks as the most popular mobile app in the United States based on audience reach. The most popular YouTube partner channels consistently attract dozens of millions of viewers and the top YouTube partner channel in the United States as of March 2019 was music label Universal Music Group (UMG), with over 50 million unique viewers. Music on YouTube Music is one of the most popular types of content on YouTube and as of 2019, half of the U.S. population used YouTube to listen to music on a weekly basis. Music videos frequently go viral and attract a large amount of attention upon release: it is not uncommon for popular releases to rack up 100 million video views within a week. Korean pop phenomenon BTS currently holds the record for the fastest viral video to reach 100 million YouTube streams. Their August 2020 release “Dynamite” only needed one day to accomplish the feat.

We provide text metadata, image frames, and thumbnails of YouTube videos classified as harmful or harmless by domain experts, GPT-4-Turbo, and crowdworkers. Harmful videos are categorized into one or more of six harm categories: Information harms (IH), Hate and Harassment harms (HH), Clickbait harms (CB), Addictive harms (ADD), Sexual harms (SXL), and Physical harms (PH).

This repository includes the text metadata and a link to external cloud storage for the image data.

Text Metadata

Folder Subfolder

Videos

Ground Truth Harmful_full_agreement(classified as harmful by all the three actors) 5,109

Harmful_subset_agreement(classified as harmful by more than two actors) 14,019

Domain Experts Harmful 15,115

Harmless 3,303

GPT-4-Turbo Harmful 10,495

Harmless 7,818

Crowdworkers (Workers from Amazon Mechanical Turk) Harmful 12,668

Harmless 4,390

Unannotated large pool

60,906

Note. The term "actor" refers to the annotating entities: domain experts, GPT-4-Turbo, and crowdworkers

Explanations about the indicators

1. Ground truth - harmful_full_agreement & harmful_subset agreement

• links

• video_id

• channel

• description

• transcript

• date

• maj_harmcat: In the full_agreement version, this represents a harm category identified by all three actors. In the subset_agreement version, it represents a harm category classified by more than two actors.

• all_harmcat: This includes all harm categories classified by any of the actors without requiring agreement. It captures all classified categories.

1. Domain Experts, GPT-4-Turbo, Crowdworkers

• links

• video_id

• channel

• description

• transcript

• date

• harmcat

1. Unannotated large pool

• links

• video_id

• channel

• description

• transcript

• date

Note. Some data from the external dataset does not include date information. In such cases, the date was marked as 1990-01-01.We retrieved transcripts using the YouTubeTranscriptApi. If a video does not have any text data in the transcript section, it means the API failed to retrieve the transcript, possibly because the video does not contain any detectable language.

Some image frames are also available in the pickle file.

Image data

The image frames and thumbnails are available at this link: https://ucdavis.app.box.com/folder/302772803692?s=d23b20snl1slwkuh4pgvjs31m7r1xae2

1. Image frames (imageframes_1-20.zip): Image frames are organized into 20 zip folders due to the large size of the image frames. Each zip folder contains subfolders named after the unique video IDs of the annotated videos. Inside each subfolder, there are 15 sequentially numbered image frames (from 0 to 14) extracted from the corresponding video. The image frame folders do not distinguish between videos classified as harmful or non-harmful.

2. Thumbnails (Thumbnails.zip): The zip folder contains thumbnails from the individual videos used in classification. Each thumbnail is named using the unique video ID. This folder does not distinguish between videos classified as harmful or harmless

Related works (in preprint)

For details about the harm classification taxonomy and the performance comparison between crowdworkers, GPT-4-Turbo, and domain experts, please see https://arxiv.org/abs/2411.05854.

Do online platforms facilitate the consumption of potentially harmful content? Using paired behavioral and survey data provided by participants recruited from a representative sample in 2020 (n=1,181), we show that exposure to alternative and extremist channel videos on YouTube is heavily concentrated among a small group of people with high prior levels of gender and racial resentment. These viewers typically subscribe to these channels (prompting recommendations to their videos) and often follow external links to them. In contrast, non-subscribers rarely see or follow recommendations to videos from these channels. Our findings suggest YouTube's algorithms were not sending people down "rabbit holes" during our observation window in 2020, possibly due to changes that the company made to its recommender system in 2019. However, the platform continues to play a key role in facilitating exposure to content from alternative and extremist channels among dedicated audiences.

The dataset is a curated collection of YouTube video URLs dedicated to data science, Python, Power BI, and Microsoft Excel. It serves as a resource for learning, tutorials, and reference materials, helping users enhance their skills in these domains.

Context 📃

I wanted to practice text classification using NLP techniques, so I thought why not practice it by generating the data myself! This way, I brushed up on my scraping techniques using Selenium, collected the data, cleaned it, and then started working on it. You can take a peek at my work Github Repository For This Dataset and Trained Models/ Results

Content 📰

The total number of videos scraped was 3600. I scraped the following things from each video: | link | title | description | category | | --- | --- | --- | --- | | Video ID | Category for which the video was scraped | Description of the video | Category for which the video was scraped. |

I queried the videos for 4 categories:

Travel Vlogs 🧳 Food 🥑 Art and Music 🎨 🎻 History 📜

Acknowledgements 🙏

I could have used a ready made API, but just for the fun of it, I scraped the data from Youtube using Selenium.

Inspiration 🦋

The data is not clean (for your enjoyment of cleaning the data!), has some missing values, and is imbalanced. Practice text classification on this dataset, you will have to learn different techniques for eg:- How to handle imbalanced classes..? While working on this dataset, you will learn a lot of different things and also get an opportunity to apply on this dataset.

Dataset for ShawGPT, a fine-tuned data science YouTube comment responder. Video link: coming soon! Blog link: coming soon!

As of December 2023, the most viewed YouTube channel in Panama was SrJinxed with more than 4.74 billion video views, followed by Mimonona Stories, with around 2.24 billion views. Additionally, Mimonona Stories was the YouTube channel with the most subscribers in Panama during that same period in time.

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

This statistic presents the average length of YouTube videos as of December 2018, sorted by category. According to the report, the average video length is at 11.7 minutes. Music content generally had the shortest video length among all categories on the platform, with an average length of 6.8 minutes per video.

The commercial videos are presenting visions of future Augmented reality applications. We analyzed 30 YouTube videos featuring AR devices in industrial manufacturing and construction. We offer the excel sheet including the list of the 30 commercial YouTube videos with information on year/title/duration/initiator/type of initiator/link/keywords in search/field of AR application/short description/notes on each of the respective videos.

What is the most subscribed YouTube channel? MrBeast made the first place in the ranking of the most-subscribed YouTube channels in January 2025. With 343 million subscribers, the U.S. based videographer and internet personality managed to surpass Indian music network T-Series, which held the number one place for several years, and sat at 284 million subscriber as of the examined period. How many hours of video are uploaded to YouTube every minute? YouTube was launched in 2005 as a platform for sharing user-generated videos such as vlogs, tutorials, or original series. The site grew rapidly and reportedly had 100 million video views per day and more than 65 thousand daily uploads only a year later. As of February 2022, more than 500 hours of video were uploaded to YouTube every minute, up from a mere 24 hours of content uploads per minute in 2010. YouTube Partner Program In the first quarter of 2024, YouTube’s ad revenue amounted to over eight billion U.S. dollars. Through its Partner Program, YouTube also rewards uploaders of popular videos with a share of the advertising revenues the content generates. This, paired with the fact that many users of the video sharing platform tend to have favorite channels that they revisit regularly, has given rise to another phenomenon: YouTube celebrities. Although some of these well-known figures were discovered on the website but then carved a successful career outside of YouTube, for many others the site is their primary platform for delivering content and staying in contact with fans, all while signing lucrative deals or promotional partnerships. Highest earning YouTubers In November 2022, MrBeast surpassed long-standing most subscribed YouTuber PewDiePie, having reached approximately 112 million subscribers. Due to the high number of subscribers and even higher number of views, these out-of-the-box stars not only have millions of fans, but also considerable earnings from their YouTube activities. In 2023, MrBeast was estimated to have earned around 82 million U.S. dollars, topping the ranking of the highest-earning YouTube creators. The ranking also included social media personality Jake Paul and Mark Fischbach, as well as Ryan Kaji from Ryan's World (formerly known as ToysReview), who started his YouTube career reviewing toys at three years old.

This data set is composed of YouTube videos links that were analyzed and the coding sheets which can be used in replication studies of the same nature.

The NYC Parks Events Listing database is used to store event information displayed on the Parks website, nyc.gov/parks. There are seven related tables that make up the this database: Events_Events table (This is the primary table that contains basic data about every event. Each record is an event.) Events_Categories (Each record is a category describing an event. One event can be in more than one category.) Events_Images (Each record is an image related to an event. One event can have more than one image.) Events_Links (Each record is a link with more information about an event. One event can have more than one link.) Events_Locations (Each record is a location where an event takes place. One event can have more than one location.) Events_Organizers (Each record contains a group or person organizing an event. One event can have more than one organizer.) Events_YouTube (Each record is a link to a YouTube video about an event. One event can have more than one YouTube video.) The Events_Events table is the primary table. All other tables can be related by joining on the event_id. This data contains records from 2013 and on. For a complete list of related datasets, please follow This Link

• Data from Amber Heard's Official Facebook Page before hidden in search from 2021
• Facebook Links Analysis from InformationTracer.com on Aquaman Change Petiton & "Amber Heard's 2020 Takeway: Adapt & Survive" YouTube Video
• Daily Scraper Data from June - July 2022 on Amber Heard with 10 hashtags

Public Research Study https://github.com/RescueSocialTech/Amber-Heard_Disinformation_Operations_Bots/tree/main/_Facebook%20Analysis

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.

This statistic presents the most popular video content categories on YouTube worldwide, ranked by market share. As of December 2018, people and blogs were the most popular YouTube content category based on share of available videos. The category accounted for 32 percent of public videos on the platform.