On June 17, 2016, Korean education brand Pinkfong released their video "Baby Shark Dance", and the rest is history. In January 2021, Baby Shark Dance became the first YouTube video to surpass 10 billion views, after snatching the crown of most-viewed YouTube video of all time from the former record holder "Despacito" one year before. "Baby Shark Dance" currently has over 15 billion lifetime views on YouTube. Music videos on YouTube “Baby Shark Dance” might be the current record-holder in terms of total views, but Korean artist Psy’s “Gangnam Style” video remained on the top spot for longest (1,689 days or 4.6 years) before ceding its spot to its successor. With figures like these, it comes as little surprise that the majority of the most popular videos on YouTube are music videos. Since 2010, all but one the most-viewed videos on YouTube have been music videos, signifying the platform’s shift in focus from funny, viral videos to professionally produced content. As of 2022, about 40 percent of the U.S. digital music audience uses YouTube Music. Popular video content on YouTube Music fans are also highly engaged audiences and it is not uncommon for music videos to garner significant amounts of traffic within the first 24 hours of release. Other popular types of videos that generate lots of views after their first release are movie trailers, especially superhero movies related to the MCU (Marvel Cinematic Universe). The first official trailer for the upcoming film “Avengers: Endgame” generated 289 million views within the first 24 hours of release, while the movie trailer for Spider-Man: No Way Home generated over 355 views on the first day from release, making it the most viral movie trailer.

As of January 2025, T-Series had reached surpassed 281 billion lifetime video views, making it the most-viewed YouTube channel owner of all time. Popular children's rhymes and song channel Cocomelon followed, with 194 billion lifetime views. Several children-themed channels such as Like Nastya, Kids Diana Show, and Vlad and Niki appeared in the ranking. YouTube is one of the most beloved platforms for children and teens In recent years, video content not only has become one of the most popular types of online formats for children but also one of the highest-performing online categories. The most viewed video on YouTube has been for several years "Baby Shark Dance," published by Korean education brand Pinkfong in June 2016. The catchy rhyme was the first YouTube video to reach and surpass 10 billion views, and as of January 2024 was sitting at 13.93 billion global views. YouTube channels that propose content for children and young teens are also among the most subscribed channels on the platform, with 3D-animation channel Cocomelon counting almost 188 million subscribers worldwide as of January 2025. Children mobile video app usage According to a survey of global parents conducted in September 2021, watching YouTube videos was the most common mobile activity for 54 percent of children, second only to online gaming. Additionally, watching movies and watching content on social media were indicated by 30 percent and 20 percent of parents as usual activities for their children to engage with on mobile devices. In the United States and in the United Kingdom, YouTube and Netflix were the most popular video apps for children to engage with.

This dataset is a daily record of the top trending YouTube videos and it will be updated daily.

Context

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.

Content

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.

Acknowledgements

This dataset was collected using the YouTube API. This dataset is the updated version of Trending YouTube Video Statistics.

Inspiration

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!

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.

As of January 2025, the most viewed YouTube channel in Mexico was YOLO AVENTURAS, with more than ***** billion video views. It was followed by Masha y el Oso, with around ***** billion views. YOLO AVENTURAS was also the second YouTube channel with most subscribers in Mexico.

All-Time Most Viewed Music Videos – This indicator measures the total accumulated views (in billions) that music videos have garnered on YouTube since their upload, regardless of when they were released. It represents the overall historical popularity and enduring appeal of these videos across time.

Dataset Name: 2023 YouTube Most Viewed Top600

Description: This dataset, titled "2023YouTubeMostViewed_Top600", comprises a curated selection of the top 600 YouTube videos based on view count, specifically from the year 2023. Each entry in the dataset represents a unique video, encompassing several key metrics:

• Title: The title of the video, providing a glimpse into its content.
• Published At: The upload date and time of the video, confirming its recency within the 2023 timeframe.
• Duration: The length of the video, indicating its watch time.
• View Count: The total number of views the video has accumulated, the primary metric for its inclusion in this dataset.
• Like Count: The number of likes the video has received, offering insight into viewer engagement.
• Comment Count: The total count of comments on the video, further reflecting audience interaction.

It's important to note that while these videos are among the most viewed as of the data retrieval date, the landscape of YouTube is dynamic. View counts are continually changing, and what constitutes the 'most viewed' can fluctuate. Thus, the dataset should be seen as a snapshot of popularity and viewer engagement during a specific period in 2023, rather than an absolute ranking. This dataset is invaluable for analysis of trending content, viewer preferences, and video engagement metrics on YouTube for the year 2023.

Note: Ethically mined data from YouTube

As of June 2025, the YouTube channel with the most number of video views in Israel, was "TIKTORIKI" with some **** billion video views. The channel with the second most video views was "Yn_ Rt " with around **** billion views.

YouTube Videos and Channels Metadata

Analyze the statistical relation between videos and form a topic tree

By VISHWANATH SESHAGIRI [source]

About this dataset

This dataset contains YouTube video and channel metadata to analyze the statistical relation between videos and form a topic tree. With 9 direct features, 13 more indirect features, it has all that you need to build a deep understanding of how videos are related – including information like total views per unit time, channel views, likes/subscribers ratio, comments/views ratio, dislikes/subscribers ratio etc. This data provides us with a unique opportunity to gain insights on topics such as subscriber count trends over time or calculating the impact of trends on subscriber engagement. We can develop powerful models that show us how different types of content drive viewership and identify the most popular styles or topics within YouTube's vast catalogue. Additionally this data offers an intriguing look into consumer behaviour as we can explore what drives people to watch specific videos at certain times or appreciate certain channels more than others - by analyzing things like likes per subscribers and dislikes per views ratios for example! Finally this dataset is completely open source with an easy-to-understand Github repo making it an invaluable resource for anyone looking to gain better insights into how their audience interacts with their content and how they might improve it in the future

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How to use the dataset

How to Use This Dataset

In general, it is important to understand each parameter in the data set before proceeding with analysis. The parameters included are totalviews/channelelapsedtime, channelViewCount, likes/subscriber, views/subscribers, subscriberCounts, dislikes/views comments/subscriberchannelCommentCounts,, likes/dislikes comments/views dislikes/ subscribers totviewes /totsubsvews /elapsedtime.

To use this dataset for your own analysis:1) Review each parameter’s meaning and purpose in our dataset; 2) Get familiar with basic descriptive statistics such as mean median mode range; 3) Create visualizations or tables based on subsets of our data; 4) Understand correlations between different sets of variables or parameters; 5) Generate meaningful conclusions about specific channels or topics based on organized graph hierarchies or tables.; 6) Analyze trends over time for individual parameters as well as an aggregate reaction from all users when videos are released

Research Ideas

• Predicting the Relative Popularity of Videos: This dataset can be used to build a statistical model that can predict the relative popularity of videos based on various factors such as total views, channel viewers, likes/dislikes ratio, and comments/views ratio. This model could then be used to make recommendations and predict which videos are likely to become popular or go viral.

• Creating Topic Trees: The dataset can also be used to create topic trees or taxonomies by analyzing the content of videos and looking at what topics they cover. For example, one could analyze the most popular YouTube channels in a specific subject area, group together those that discuss similar topics, and then build an organized tree structure around those topics in order to better understand viewer interests in that area.

• Viewer Engagement Analysis: This dataset could also be used for viewer engagement analysis purposes by analyzing factors such as subscriber count, average time spent watching a video per user (elapsed time), comments made per view etc., so as to gain insights into how engaged viewers are with specific content or channels on YouTube. From this information it would be possible to optimize content strategy accordingly in order improve overall engagement rates across various types of video content and channel types

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: YouTubeDataset_withChannelElapsed.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------| | totalviews/channelelapsedtime | Ratio of total views to channel elapsed time. (Ratio) | | channelViewCount | Total number of views for the channel. (Integer) | | likes/subscriber ...

Key YouTube Statistics (Editor’s Choice) YouTube recorded 70 billion monthly active users in March 2023, which includes 55.10% of worldwide active social media users. There have been more than 14 million daily active users currently on YouTube, in the United States of America this platform is accessed by 62% of users. YouTube is touted as the second largest search engine and the second most visited website after Google. Revenue earned by YouTube in the first two quarters of 2023 is around $14.358 billion. In 2023, YouTube Premium and YouTube Music have recorded 80 million subscribers collectively worldwide. YouTube consumers view more than a billion hours of video per day. YouTube has more than 38 million active channels. In the fourth quarter of 2021, YouTube ad revenue has been $8.6 billion. Around 3 million paid subscribers to access YouTube TV. YouTube Premium has around 1 billion paid users. In 2023, YouTube was banned in countries such as China excluding Macau and Hong Kong, Eritrea, Iran, North Korea, Turkmenistan, and South Sudan. With 166 million downloads, the YouTube app has become the second most downloaded entertainment application across the world after Netflix. With 91 million downloads, YouTube Kids has become the sixth most downloaded entertainment app in the world. Nearly 90% of digital consumers access YouTube in the US, making it the most popular social network for watching video content. Over 70% of YouTube viewership takes place on its mobile application. More than 70% of YouTube video content watched by people is suggested by its algorithm. The average duration of a video on YouTube is 12 minutes. An average YouTube user spends 20 minutes and 23 seconds on the platform daily. Around 28% of YouTube videos that are published by popular channels are in the English language. 77% of YouTube users watch comedy content on the platform. With 247 million subscribers, T-Series has become the most subscribed channel on YouTube. Around 50 million users log on to YouTube every day. YouTube's biggest concurrent views record has been at 2.3 billion from when SpaceX has gone live on the platform to unveil Falcon Heavy Rocket. The majority of YouTube users are in the age group of 15 to 35 years in the US. The male-female ratio of YouTube users is 11:9. Apple INC. has been touted as the biggest advertiser on YouTube in 2020 spending $237.15 million. YouTube produced total revenue of $19.7 billion in 2020. As of 2021, the majority of YouTube users (467 million) are from India. It is the most popular platform in the United States with 74 percent of adult users. YouTube contributes to nearly 25% of mobile traffic worldwide. Daily live streaming on YouTube has increased by 45% in total in 2020. In India, around 225 million people are active on the platform each hour as per the 2021 statistics. YouTube Usage and Viewership Statistics #1. YouTube accounts for more than 2 billion monthly active users Around 2.7 billion users log on to YouTube each month. The number of monthly active users of YouTube is expected to grow even further. #2. Around 14.3 billion people visit the platform every month The number of YouTube visitors is far higher compared to Facebook, Amazon, and Instagram. #3. YouTube is accessible across 100 countries in 80 languages. The platform is widely available across different communities and nations. #4. 53.9% of YouTube users are men and 46.1% of women use the platform As of 2023 statistics, 53.9% of men use the platform and 46.1% of women over 18 years are on YouTube. The share in the number of males and females is 1.38 billion and 1.18 billion respectively. Age Group Male Female 18 to 24 8.5% 6% 25 to 34 11.6% 8.6% 35 to 44 9% 7.5% 45 to 54 6.2% 5.7% 55 to 64 4.4% 4.5% Above 65 4.3% 5.4% #5. 99% of YouTube users are active on other social media networks as well. Fewer than 1% of YouTube users are solely dependent on the platform. #6. Users spend around 20 minutes and 23 seconds per day on YouTube on average It is quite a generous amount of time spent on any social network platform. #7. YouTube is the second most visited site worldwide With more than 14 billion visits per month, YouTube has become the second most visited site in the world. However, its parent company Google is the most visited site across the globe. As per the statistics, YouTube is the third most popular searched word on Google. #8. 694000 hours of video content are streamed on YouTube per minute YouTube has outweighed Netflix as well in terms of streaming video content. #9. Over 81% of total internet users have accessed YouTube #10. Nearly 450 million hours of video content are uploaded on YouTube each hour More than 5 billion videos are watched on YouTube per day. #11. India has the maximum numb

The YouTube channel with the highest number of video views in Romania, as of **********, was a channel with songs and cartoons for children called "TraLaLa - Cantece si desene animate pentru copii" which recorded **** billion views. Another YouTube channel for children, called "CanteceGradinita.ro" ranked sixth, with over ***** billion views. Nevertheless, the top ten YouTube channels in Romania, by number of video views was dominated by music channels such as “RotonMusicTv”, “INNA”, or “AmmA Music”.

YouTube usage in Romania

In 2021, YouTube recorded a weekly usage rate of ** percent, making it the second most used social media platform in Romania, after Facebook. These results were in line with a survey conducted by iSense Solutions, which outlined that YouTube was again the second most popular social media platform in Romania after Facebook, with over ** percent of respondents having a YouTube account. At the same time, the video content platform ranked fourth by market share, after Facebook and Pinterest.

Social media usage in Romania

Several surveys showed that the most preferred online activity of Romanian people was either using messenger apps or social media platforms. In Romania, Facebook remained the most used social media platform both by market share and number of users. However, given its high usage rate and influence in the rest of Europe, as well as on other continents, Instagram is slowly gaining popularity in Romania too. As a result, by **********, there were over **** million Instagram accounts.

Context

As YouTube becomes one of the most popular video-sharing platforms, YouTuber is developed as a new type of career in recent decades. YouTubers earn money through advertising revenue from YouTube videos, sponsorships from companies, merchandise sales, and donations from their fans. In order to maintain a stable income, the popularity of videos become the top priority for YouTubers. Meanwhile, some of our friends are YouTubers or channel owners in other video-sharing platforms. This raises our interest in predicting the performance of the video. If creators can have a preliminary prediction and understanding on their videos’ performance, they may adjust their video to gain the most attention from the public.

You have been provided details on videos along with some features as well. Can you accurately predict the number of likes for each video using the set of input variables?

Content

Train Set

video_id -> Identifier for each video

title -> Name of the Video on Youtube

channel_title -> Name of the Channel on Youtube

category_id -> Category of the Video (anonymous)

publish_date -> The date video was published

tags -> Different tags for the video

views -> Number of views received by the Video

dislikes -> Number of dislikes on the Video

comment_count -> Number on comments on the Video

description -> Textual description of the Video

country_code -> Country from which the Video was published

likes -> Number of Likes on the video

Acknowledgements

Thank You Analytics Vidhya for providing this dataset.

Panda Shorts, a channel that focuses on comedy reactions, was the most viewed channel on YouTube in Sweden, as of January 2025. The videos, focused mainly on reactions and challenges, were viewed over **** billion times. The next channel in the ranking was Avicii, with more than **** billion views. This was followed with another channel dedicated to Avicii, and AviciiOfficialVEVO had around **** billion video views for the evaluated period. Furthermore, Panda Shorts had the channel with the highest number of subscribers, reaching over **** million in January 2025. Among the other Swedish YouTube channels with the most subscribers, were ABBA and Family Playlab.  How much do YouTubers in Sweden earn? A study, conducted in 2020, analyzed the YouTubers in Sweden, by their influence and income. A so-called micro influencer would make minimum ************* Swedish kronor per video, while a macro influencer would make between ** thousand and ** thousand that year. An icon influencer could make up to *** thousand Swedish kronor.  YouTube activities Watching entertaining videos was what most Swedes used YouTube for in 2022. Other popular activities were watching educational videos, listening to music, watching documentaries, and subscribing to channels. Three percent of the respondents in 2022, said, they created and published their own videos.

This dataset contains two files for analyzing the relationship between the popularity of a certain video and the most relevant/liked comments of said video.

File Descriptions

videos-stats.csv: This file contains some basic information about each video, such as the title, likes, views, keyword, and comment count.

comments.csv: For each video in videos-stats.csv, comments.csv contains the top ten most relevant comments as well as said comments' sentiments and likes.

Column Descriptions

videos-stats.csv: - Title: Video Title. - Video ID: The Video Identifier. - Published At: The date the video was published in YYYY-MM-DD. - Keyword: The keyword associated with the video. - Likes: The number of likes the video received. If this value is -1, the likes are not publicly visible. - Comments: The number of comments the video has. If this value is -1, the video creator has disabled comments. - Views: The number of views the video got.

comments.csv: - Video ID: The Video Identifier. - Comment: The comment text. - Likes: The number of likes the comment received. - Sentiment: The sentiment of the comment. A value of 0 represents a negative sentiment, while values of 1 or 2 represent neutral and positive sentiments respectively.

Applicability

• Sentiment Analysis with comments
• Text Generation with comments
• Predicting video likes from comment information
• Popularity Analysis by Keyword
• Popularity Analysis
• Prediction video views from comment information/video statistics
• In-depth EDA of the Data

Video communication has played a key role in relaying important and complex information on the COVID-19 pandemic to the general public. The aim of the present study is to compare Norwegian health authorities’ and WHO’s use of video communication during the COVID-19 pandemic to the most viewed COVID-19 videos on YouTube, in order to identify how videos created by health authorities measure up to contemporary video content, both creatively and in reaching video consumers. Through structured search on YouTube we found that Norwegian health authorities have published 26 videos, and the WHO 29 videos on the platform. Press briefings, live videos, news reports, and videos recreated/translated into other languages than English or Norwegian, were not included. A content analysis comparing the 55 videos by the health authorities to the 27 most viewed videos on COVID-19 on YouTube demonstrates poor reach of health authorities’ videos in terms of views and it elucidates a clear creative gap. While the videos created by various YouTube creators communicate using a wide range of creative presentation means (such as professional presenters, contextual backgrounds, advanced graphic animations, and humour), videos created by the health authorities are significantly more homogenous in style often using field experts or public figures, plain backgrounds or PowerPoint style animations. We suggest that further studies into various creative presentation means and their influence on reach, recall, and on different groups of the population, are carried out in the future to evaluate specific factors of this creative gap.

This dataset and supplementary tables are released in conjunction with the TPDL 2025 paper titled “How Retracted Research Persists on YouTube: Retraction Severity, Visibility, and Disclosure.” They provide detailed information used in the analysis to promote transparency, ensure reproducibility, and facilitate future studies on scholarly communication and retractions.

The dataset contains the following files:

Filename	Data Format	Description
01_dataset_scholarly_references_on_YouTube.json.gz	JSON Lines	An integrated dataset of scholarly references in YouTube video descriptions, covering videos posted up to the end of December 2023. This dataset combines the Altmetric dataset and the YA Domain Dataset and is the basis for identifying references to retracted articles. This dataset contains 743,529 scholarly references (386,628 unique DOIs) found in 322,521 YouTube videos uploaded by 77,974 channels.
02_dataset_references_to_retracted_articles_on_YouTube.json.gz	JSON Lines	A dataset of retracted articles referenced in YouTube videos, used as the primary source for analysis in this paper. The dataset was created by cross-referencing the integrated reference dataset with the Retraction Watch database. It includes metadata such as DOI, article title, retraction reason, and severity classification (Severe, Moderate, or Minor) based on Woo and Walsh (2024), along with video- and channel-level statistics (e.g., view counts and subscriber counts) retrieved via the YouTube Data API v3 as of April 22, 2025. This dataset contains 1,002 retracted articles (360 unique DOIs) found in 956 YouTube videos uploaded by 714 channels.
03_full_list_table3_sorted_by_reference_count_retracted_articles_on_YouTube.json.gz	JSON Lines	Complete list corresponding to Table 3, "Top 7 retracted articles ranked by the number of YouTube videos in which they are referenced." in the paper.
04_full_list_table5_top10_most-viewed_video.json.gz	JSON Lines	Complete list corresponding to Table 5, "Top 10 most-viewed YouTube videos that reference retracted articles, sorted by video view count." in the paper.
05_detailed_manual_coding_40_sampled_retracted_articles.xlsx	XLSX	This file provides detailed annotations for a manually coded sample of 40 YouTube videos referencing retracted scholarly articles. The sample includes 10 randomly selected videos from each of the four analytical groups categorized by publication timing (before/after retraction) and retraction severity (Moderate/Severe). The file includes reference stance for each video, visual/verbal mention of the article, and relevant timestamps when applicable. This dataset supplements the manual analysis results presented in Tables 6 and 7 in paper.

Due to concerns over potential misuse (e.g., identification or harassment of individual content creators), this dataset is not made publicly available.
Researchers who wish to use this dataset for scholarly purposes may contact the authors to request access.

References

• Woo, S., Walsh, J.P.: On the shoulders of fallen giants: What do references to retracted research tell us about citation behaviors? Quantitative Science Studies 5(1), 1–30 (2024). https://doi.org/10.1162/qss_a_00303
• Kikkawa, J., Takaku, M.: How Retracted Article Persists on YouTube: Retraction Severity, Visibility, and Disclosure. Accepted for publication in the Proceedings of the 29th International Conference on Theory and Practice of Digital Libraries (TPDL 2025).
• Accepted Papers (TPDL2025) - https://tpdl2025.github.io/Program/accepted_papers.html

Fundings

JSPS KAKENHI Grant Numbers JP22K18147 and JP23K11761.

In 2023, French content creator and internet personalities Squeezie had the most watched video on the YouTube platform. His "Who is the impostor" video generated approximately 26.1 million views among users. The survival video published by Inoxtag and titled "48H to Survive a Zombie Invasion" was the second most watched video published by a French creator on YouTube in 2023.

As of June 2024, Al Fondo hay Sitio - AFHS was the most viewed YouTube channel in Peru, with more than **** billion video views. It was followed by América Televisión with approximately *** billion views. Furthermore, accessing social media platforms is among the most popular online activities in Latin America altogether.

As of June 2024, the most viewed YouTube channel in Latin America was El Reino Infantil, from Argentina, with more than 60.77 billion video views. Brazilian channels Canal KondZilla, Galinha Pintadinha, GR6 Explode, and Maria Clara & JP ranked second, third, fifth, and sixth in the region, with around 38.33 billion, 32.4 billion, 28.25 billion, and 27.84 billion views, respectively. Meanwhile, Argentinian channel La Granja de Zenón ranked fourth, with 31.32 billion views.

As of December 2023, the most viewed YouTube channel in Bolivia was Edson FDB, which had reached nearly 890 million video views, followed by Vesalius M.D with about 655 million views. In in December 2023, almost 63 percent of the web traffic in Bolivia was concentrated via mobile devices.