YouTube Trending Videos Dataset

Exploring YouTube Trending Videos

By dskl [source]

About this dataset

Moreover it also reveals various engagement metrics such as the number of views the video has received, likes and dislikes it has garnered from viewership. Additionally information related to comment count on particular videos enables analysis regarding viewer interaction and response. Furthermore this dataset describes whether comments or ratings are disabled for a particular video allowing examination into how these factors impact engagement.

By exploring this dataset in-depth marketers can gain valuable insights into identifying trends in content popularity across different countries while taking into account timing considerations based on published day of week. It also opens up avenues for analyzing public sentiment towards specific videos based on likes vs dislikes ratios and comment count which further aids in devising suitable marketing strategies.

Overall,this informative dataset serves as an invaluable asset for researchers,data analysts,and marketers alike who strive to gain deeper understanding about trending video patterns,relevant metrics influencing content virality,factors dictating viewer sentiments,and exploring new possibilities within digital marketing space leveraging YouTube's wide reach

How to use the dataset

How to Use This Dataset: A Guide

In this guide, we will walk you through the different columns in the dataset and provide insights on how you can explore the popularity and engagement of these trending videos. Let's dive in!

Column Descriptions:

• title: The title of the video.
• channel_title: The title of the YouTube channel that published the video.
• publish_date: The date when the video was published on YouTube.
• time_frame: The duration of time (e.g., 1 day, 6 hours) that the video has been trending on YouTube.
• published_day_of_week: The day of week (e.g., Monday) when the video was published.
• publish_country: The country where the video was published.
• tags: The tags or keywords associated with the video.
• views: The number of views received by a particular video
• likes: Number o likes received per each videos
• dislike: Number dislikes receives per an individual vidoe 11.comment_count: number of comments

Popular Video Insights:

To gain insights into popular videos based on this dataset, you can focus your analysis using these columns:

title, channel_title, publish_date, time_frame, and** publish_country**.

By analyzing these attributes together with other engagement metrics such as views ,likes,**dislikes,**comments),comment_count you can identify trends in what type content is most popular both globally or within specific countries.

For instance: - You could analyze which channels are consistently publishing trending videos - Explore whether certain types of titles or tags are more likely to attract views and engagement. - Determine if certain days of the week or time frames have a higher likelihood of trending videos being published.

Engagement Insights:

To explore user engagement with the trending videos, you can focus your analysis on these columns:

likes, dislikes, comment_count

By analyzing these attributes you can get insights into how users are interacting with the content. For example: - You could compare the like and dislike ratios to identify positively received videos versus those that are more controversial. - Analyze comment counts to understand how users are engaging with the content and whether comments being disabled affects overall

Research Ideas

• Analyzing the popularity and engagement of trending videos: By analyzing the number of views, likes, dislikes, and comments, we can understand which types of videos are popular among YouTube users. We can also examine factors such as comment count and ratings disabled to see how viewers engage with trending videos.
• Understanding video trends across different countries: By examining the publish country column, we can compare the popularity of trending videos in different countries. This can help content creators or marketers understand regional preferences and tailor their content strategy accordingly.
• Studying the impact of video attributes on engagement: By exploring the relationship between video attributes (such as title, tags, publish day) and engagement metrics (views, likes), we can identify patterns or trends that influence a video's success on YouTube. This information can be...

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.

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.

Description: This dataset contains detailed information about videos from various YouTube channels that specialize in data science and analytics. It includes metrics such as views, likes, comments, and publication dates. The dataset consists of 22862 rows, providing a robust sample for analyzing trends in content engagement, popularity of topics over time, and comparison of channels' performance.

Column Descriptors:

Channel_Name: The name of the YouTube channel. Title: The title of the video. Published_date: The date when the video was published. Views: The number of views the video has received. Like_count: The number of likes the video has received. Comment_Count: The number of comments on the video.

This dataset contains information from the following YouTube channels:

['sentdex', 'freeCodeCamp.org' ,'CampusX', 'Darshil Parmar',' Keith Galli' ,'Alex The Analyst', 'Socratica' , Krish Naik', 'StatQuest with Josh Starmer', 'Nicholas Renotte', 'Leila Gharani', 'Rob Mulla' ,'Ryan Nolan Data', 'techTFQ', 'Dataquest' ,'WsCube Tech', 'Chandoo', 'Luke Barousse', 'Andrej Karpathy', 'Thu Vu data analytics', 'Guy in a Cube', 'Tableau Tim', 'codebasics', 'DeepLearningAI', 'Rishabh Mishra' 'ExcelIsFun', 'Kevin Stratvert' ' Ken Jee','Kaggle' , 'Tina Huang']

This dataset can be used for various analyses, including but not limited to:

Identifying the most popular videos and channels in the data science field.

Understanding viewer engagement trends over time.

Comparing the performance of different types of content across multiple channels.

Performing a comparison between different channels to find the best-performing ones.

Identifying the best videos to watch for specific topics in data science and analytics.

Conducting a detailed analysis of your favorite YouTube channel to understand its content strategy and performance.

Note: The data is current as of the date of extraction and may not reflect real-time changes on YouTube. For any analyses, ensure to consider the date when the data was last updated to maintain accuracy and relevance.

Youtube-vis is a video instance segmentation dataset. It contains 2,883 high-resolution YouTube videos, a per-pixel category label set including 40 common objects such as person, animals and vehicles, 4,883 unique video instances, and 131k high-quality manual annotations.

The YouTube-VIS dataset is split into 2,238 training videos, 302 validation videos and 343 test videos.

No files were removed or altered during preprocessing.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('youtube_vis', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

📺 YouTube-Commons 📺

YouTube-Commons is a collection of audio transcripts of 2,063,066 videos shared on YouTube under a CC-By license.

  Content

The collection comprises 22,709,724 original and automatically translated transcripts from 3,156,703 videos (721,136 individual channels). In total, this represents nearly 45 billion words (44,811,518,375). All the videos where shared on YouTube with a CC-BY license: the dataset provide all the necessary provenance information… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/YouTube-Commons.

Youtube Dataset 2

## Overview

Youtube Dataset 2 is a dataset for object detection tasks - it contains Cars annotations for 520 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Context

I've been creating videos on YouTube since November of 2017 (https://www.youtube.com/c/KenJee1) with the mission of making data science accessible to more people. One of the best ways to do this is to tell stories and working on projects. This is my attempt at my first community project. I am making my YouTube data available for everyone to help better understand the growth of my YouTube community and think about ways that it could be improved! I would love for everyone in the community feel like they had some hand in contributing to the channel.

Announcement Video: https://youtu.be/YPph59-rTxA

I will be sharing my favorite projects in a few of my videos (with permission of course), and would also like to give away a few small prizes to the top featured notebooks. I hope you have fun with the analysis, I'm interested in seeing what you find in the data!

For those looking for a place to start, some things I'm thinking about are: - What are the themes of the comment data? - What types of video titles and thumbnails drive the most traffic? - Who is my core audience and what are they interested in? - What types of videos have lead to the most growth? - What type of content are people engaging with the most or watching the longest?

Some advanced projects could be: - Creating a chat bot to respond to common comments with videos where I have addressed a topic - Pulling sentiment from thumbnails and titles and comparing that with performance

Data I would like to add over time - Video descriptions - Video subtitles - Actual video data

Content

There are four files in this repo. The relevant data included in most of them is from Nov 2017 - Jan 2022. I gathered some of this data via the YouTube API and the rest from my specific analytics.

1) Aggregated Metrics By Video - This has all the topline metrics from my channel from its start (around 2015 to Jan 22 2022). I didn't post my first video until around 2) Aggregated Metrics By Video with Country and Subscriber Status - This has the same data as aggregated metrics by video, but it includes dimensions for which country people are viewing from and if the viewers are subscribed to the channel or not. 3) Video Performance Over Time - This has the daily data from each of my videos. 4) All Comments - This is all of my comment data gathered from the YouTube API. I have anonymized the users so don't worry about your name showing up!

Acknowledgements

This obviously wouldn't be possible without all of the wonderful people who watch and interact with my videos! I'm incredibly grateful for you all and I'm so happy I can share this project with you!

License

I collected this data from the YouTube API and through my own google analytics. Thus use of it must uphold the YouTube API's terms of service: https://developers.google.com/youtube/terms/api-services-terms-of-service

The YouTube Thumbnail Dataset features a large collection of top-performing video thumbnails from over 90 YouTube channels. It includes metadata such as video IDs, views, likes, comments, and duration for AI and media analysis applications.

OpenDV-YouTube

This is the dataset repository of OpenDV-YouTube language annotations, including context and command. For more details, please refer to GenAD project and OpenDV-YouTube.

  Usage

To use the annotations, you need to first download and prepare the data as instructed in OpenDV-YouTube. Note that we recommend to process the dataset in Linux environment since Windows may have issues with the file paths. You can use the following code to load in annotations… See the full description on the dataset page: https://huggingface.co/datasets/OpenDriveLab/OpenDV-YouTube-Language.

Youtube Videos

## Overview

Youtube Videos is a dataset for object detection tasks - it contains Cars annotations for 3,842 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

FineVideo

FineVideo Description Dataset Explorer Revisions Dataset Distribution

How to download and use FineVideo Using datasets Using huggingface_hub Load a subset of the dataset

Dataset StructureData Instances Data Fields

Dataset Creation License CC-By Considerations for Using the Data Social Impact of Dataset Discussion of Biases

Additional Information Credits Future Work Opting out of FineVideo Citation Information

Terms of use for FineVideo… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFV/finevideo.

Real+fake+wild+youtube

## Overview

Real+fake+wild+youtube is a dataset for object detection tasks - it contains Smoke annotations for 1,340 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Sampel Youtube

## Overview

Sampel Youtube is a dataset for object detection tasks - it contains Cigarette 2nwS annotations for 450 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The UCF11 dataset, previously known as the YouTube Action Data Set, is a comprehensive collection of 1,160 videos categorized into 11 distinct action classes. The actions represented include:

1. Basketball shooting
2. Biking/Cycling
3. Diving
4. Golf swinging
5. Horseback riding
6. Soccer juggling
7. Swinging
8. Tennis swinging
9. Trampoline jumping
10. Volleyball spiking
11. Walking with a dog

Dataset Structure:

The dataset is organized into folders, each representing one of the 11 action categories. Within each category folder, videos are grouped into 25 groups, with each group containing more than four action clips. The folder structure is as follows:

UCF11/
│
├── Basketball/
│  ├── Group01/
│  │  ├── video01.mpg
│  │  ├── video02.mpg
│  │  └── ...
│  ├── Group02/
│  └── ...
├── Biking/
├── Diving/
└── ...

Each video file is named in a manner indicating its group and sequential number within the group. The videos within a group share common features such as the same actor, similar background, and viewpoint, which is conducive for studying action recognition under varied conditions.

File Format:

The videos are in ms mpeg4 format, and it's recommended to have the appropriate codec (e.g., K-lite Codec Pack) for access. In the updated version of UCF11, all videos are converted to 29.97 fps (mpg), and annotations have been refined to address previous inconsistencies.

Usage:

This dataset is well-suited for projects in computer vision, machine learning, and action recognition, providing a substantive resource for training and testing algorithms in real-world conditions.

Related Publications: - Jingen Liu, Jiebo Luo, and Mubarak Shah, "Recognizing Realistic Actions from Videos 'in the Wild'", CVPR 2009. - Jingen Liu, Yang Yang, and Mubarak Shah, "Learning Semantic Visual Vocabularies using Diffusion Distance", CVPR 2009.

YouTube flows

Youtube

## Overview

Youtube is a dataset for object detection tasks - it contains Sidewalk Objects annotations for 507 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

We only provide video URLs. Text/Captions are generated by BLIP-2. Please follow open-source agreement for any usage. DATA PREPARATION

Download youtube videos to the folder '$workdir/download_videos' with the urls provided in metafiles, and name the videos with their video_id. e.g. url: https://www.youtube.com/watch?v=--4M68p_Loc

$workdir download_videos --4M68p_Loc.mp4

We do not provide an official script for downloading YouTube videos. You may consider using the open-source… See the full description on the dataset page: https://huggingface.co/datasets/Jonathan916/my-cool-dataset.

AVAS YouTube

## Overview

AVAS YouTube is a dataset for object detection tasks - it contains Avas GoSg annotations for 5,072 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Indonesian Youtube

Source code at https://github.com/mesolitica/malaysian-dataset/tree/master/speech/indonesian-youtube

  how to download

huggingface-cli download --repo-type dataset
--include '*.z*'
--local-dir './'
malaysia-ai/indonesian-youtube

wget https://www.7-zip.org/a/7z2301-linux-x64.tar.xz tar -xf 7z2301-linux-x64.tar.xz ~/7zz x mp3-16k.zip -y -mmt40

  Licensing

All the videos, songs, images, and graphics used in the video belong to their… See the full description on the dataset page: https://huggingface.co/datasets/malaysia-ai/indonesian-youtube.