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.

Motivation

Study how YouTube videos become viral or, more in general, how they evolve in terms of views, likes and subscriptions is a topic of interest in many disciplines. With this dataset you can study such phenomena, with statistics about 1 million YouTube videos. The information was collected in 2013 when YouTube was exposing the data publicly: they removed this functionality in the years and now it's possible to have such statistics only to the owner of the video. This makes this dataset unique.

Context

This Dataset has been generated with YOUStatAnalyzer, a tool developed by myself (Mattia Zeni) when I was working for CREATE-NET (www.create-net.org) within the framework of the CONGAS FP7 project (http://www.congas-project.eu). For the project we needed to collect and analyse the dynamics of YouTube videos popularity. The dataset contains statistics of more than 1 million Youtube videos, chosen accordingly to random keywords extracted from the WordNet library (http://wordnet.princeton.edu).

The motivation that led us to the development of the YOUStatAnalyser data collection tool and the creation of this dataset is that there's an active research community working on the interplay among user individual preferences, social dynamics, advertising mechanisms and a common problem is the lack of open large-scale datasets. At the same time, no tool was present at that time. Today, YouTube removed the possibility to visualize these data on each video's page, making this dataset unique.

When using our dataset for research purposes, please cite it as:

@INPROCEEDINGS{YOUStatAnalyzer, author={Mattia Zeni and Daniele Miorandi and Francesco {De Pellegrini}}, title = {{YOUStatAnalyzer}: a Tool for Analysing the Dynamics of {YouTube} Content Popularity}, booktitle = {Proc. 7th International Conference on Performance Evaluation Methodologies and Tools (Valuetools, Torino, Italy, December 2013)}, address = {Torino, Italy}, year = {2013} }

Content

The dataset contains statistics and metadata of 1 million YouTube videos, collected in 2013. The videos have been chosen accordingly to random keywords extracted from the WordNet library (http://wordnet.princeton.edu).

Dataset structure

The structure of a dataset is the following: { u'_id': u'9eToPjUnwmU', u'title': u'Traitor Compilation # 1 (Trouble ...', u'description': u'A traitor compilation by one are ...', u'category': u'Games', u'commentsNumber': u'6', u'publishedDate': u'2012-10-09T23:42:12.000Z', u'author': u'ServilityGaming', u'duration': u'208', u'type': u'video/3gpp', u'relatedVideos': [u'acjHy7oPmls', u'EhW2LbCjm7c', u'UUKigFAQLMA', ...], u'accessControl': { u'comment': {u'permission': u'allowed'}, u'list': {u'permission': u'allowed'}, u'videoRespond': {u'permission': u'moderated'}, u'rate': {u'permission': u'allowed'}, u'syndicate': {u'permission': u'allowed'}, u'embed': {u'permission': u'allowed'}, u'commentVote': {u'permission': u'allowed'}, u'autoPlay': {u'permission': u'allowed'} }, u'views': { u'cumulative': { u'data': [15.0, 25.0, 26.0, 26.0, ...] }, u'daily': { u'data': [15.0, 10.0, 1.0, 0.0, ..] } }, u'shares': { u'cumulative': { u'data': [0.0, 0.0, 0.0, 0.0, ...] }, u'daily': { u'data': [0.0, 0.0, 0.0, 0.0, ...] } }, u'watchtime': { u'cumulative': { u'data': [22.5666666667, 36.5166666667, 36.7, 36.7, ...] }, u'daily': { u'data': [22.5666666667, 13.95, 0.166666666667, 0.0, ...] } }, u'subscribers': { u'cumulative': { u'data': [0.0, 0.0, 0.0, 0.0, ...] }, u'daily': { u'data': [-1.0, 0.0, 0.0, 0.0, ...] } }, u'day': { u'data': [1349740800000.0, 1349827200000.0, 1349913600000.0, 1350000000000.0, ...] } }

From the structure above is possible to see which fields an entry in the dataset has. It is possible to divide them into 2 sections:

1) Video Information.

_id -> Corresponding to the video ID and to the unique identifier of an entry in the database. title -> Te video's title. description -> The video's description. category -> The YouTube category the video is inserted in. commentsNumber -> The number of comments posted by users. publishedDate -> The date the video has been published. author -> The author of the video. duration -> The video duration in seconds. type -> The encoding type of the video. relatedVideos -> A list of related videos. accessControl -> A list of access policies for different aspects related to the video.

2) Video Statistics.

Each video can have 4 different statistics variables: views, shares, subscribers and watchtime. Recent videos have all of them while older video can have only the 'views' variable. Each variable has 2 dimensions, daily and cumulative.

`views -> number of views collected by the vi...

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.

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

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.

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.

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

YouTube was launched in 2005. It was founded by three PayPal employees: Chad Hurley, Steve Chen, and Jawed Karim, who ran the company from an office above a small restaurant in San Mateo. The first...

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 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!

During the fourth quarter of 2024, around 53.8 percent of videos removed from YouTube were deleted due to child safety reasons. This represents a decrease from the previous quarter, when the videos removed from the platform due to this reason made-up around 56.5 percent of the total removed videos. During the last measured quarter, another one percent of flagged videos were removed due to spam or misleading content.

Introduction

YouTube Statistics: YouTube dominates the digital landscape with 2.70 billion monthly active users among the world population in mid-2025, making it the second-largest search engine after Google and the second social platform, following Facebook, across the world.

People watch more than 1 billion hours of video on YouTube, that’s a million years of attention span. With over 20 million new videos uploaded to the platform every day, the YouTube content ecosystem is practically endless. Short-form video lovers have not been ignored.

With an astonishing 70 billion views a day on YouTube shorts, these viewers are generating a new level of interactions and engagement across the platform. Of course, mobile dominates; 63% of watch time happens on mobile devices. With over 100 million subscribers to YouTube Premium and YouTube Music, in addition to free, YouTube is indeed a premium entertainment platform.

This both the original .tfrecords and a Parquet representation of the YouTube 8 Million dataset. YouTube-8M is a large-scale labeled video dataset that consists of millions of YouTube video IDs, with high-quality machine-generated annotations from a diverse vocabulary of 3,800+ visual entities. It comes with precomputed audio-visual features from billions of frames and audio segments, designed to fit on a single hard disk. This dataset also includes the YouTube-8M Segments data from June 2019. This dataset is 'Lakehouse Ready'. Meaning, you can query this data in-place straight out of the Registry of Open Data S3 bucket. Deploy this dataset's corresponding CloudFormation template to create the AWS Glue Catalog entries into your account in about 30 seconds. That one step will enable you to interact with the data with AWS Athena, AWS SageMaker, AWS EMR, or join into your AWS Redshift clusters. More detail in (the documentation)[https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/README.md.

The global number of Youtube users in was forecast to continuously increase between 2024 and 2029 by in total ***** million users (+***** percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach *** 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 *** 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.

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 ...

As of February 2025, ** percent of the YouTube global audience was composed of male users aged between 25 and 34 years, as well as around *** percent of female users of the same age. Male users aged between 35 and 44 years on the platform accounted for **** percent of the total, while women of the same age using YouTube had an audience share of *** percent in the examined period. YouTube’s global popularity The number of monthly active users on YouTube reached almost *** billion in April 2024, making it the second most popular social network on the internet. The platform's popularity spans all over the world, with India and the United States having the largest YouTube audiences. As of April 2024, the audience of YouTube in India was around *** million, while the United States recorded a YouTube audience of around *** million users.

YouTube’s digital revenues One of YouTube's leading monetization methods include advertising, with the company generating around **** billion U.S. dollars in the first quarter of 2024. Additionally, the platform generated over ** million dollars in the United States through in-app purchases, as well as over **** million U.S. dollars in revenues from mobile app users in Japan.

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.

Introduction

YouTube Statistics: YouTube was founded on April 14th, 2005, by three PayPal employees, Jawed Karim, Steve Chen, and Chad Hurley. They started the company in an office above a small restaurant in San Mateo. The platform now has almost 2.49 billion active users monthly and has grown significantly over the years. 47% of internet users worldwide use YouTube each month.

YouTube serves as both a social media platform and a search engine, allowing direct interaction with the audience and the creation of content that can be easily found by users. Let's explore more YouTube statistics.

This dataset contains data related to most watched YouTube videos till April 2024 . This contains different columns namely views,artist,channel,etc. The data is ranked on the basis of number of views.

1) Data Introduction • The YouTube Clickbait Classification dataset consists of video titles and statistics aimed at classifying videos as clickbait or not clickbait. This dataset includes attributes such as video ID, title, views, likes, dislikes, and favorites, providing a basis for binary classification tasks to identify misleading content.

2) Data Utilization (1) YouTube Clickbait data has characteristics that: • It includes detailed statistics for each video, such as views, likes, dislikes, and favorites, alongside the video titles. This information helps in understanding the engagement metrics and identifying patterns associated with clickbait content. (2) YouTube Clickbait data can be used to: • Content Analysis: Assists in developing models to classify videos as clickbait or not, helping in curating quality content and improving user experience on video platforms. • Marketing and SEO: Supports the development of strategies to enhance video reach and engagement while avoiding deceptive practices, aiding in ethical content marketing and search engine optimization.