Regional YouTube Viral Content Dataset

Columns

• country: The nation where the video was published.
• video_id: A unique identification number assigned to each video.
• title: The name of the video.
• publishedAt: The publication date of the video.
• channelId: The unique identification number for the channel that published the video.
• channelTitle: The name of the channel that published the video.
• categoryId: The category identification number of the video (e.g., '10' for 'music').
• trending_date: The date on which the video was observed to be trending.
• tags: Keywords or phrases associated with the video.
• view_count: The total number of views the video has accumulated.
• comment_count: The total number of comments received on the video.
• thumbnail_link: The URL for the image displayed before the video is played.
• comments_disabled: A boolean indicator showing if comments are disabled for the video.
• ratings_disabled: A boolean indicator showing if ratings (likes/dislikes) are disabled for the video.
• description: The explanatory text provided below the video.

Distribution

• 06/04/2022 - 06/08/2022: 31 records
• 06/08/2022 - 06/11/2022: 56 records
• 06/11/2022 - 06/15/2022: 57 records
• 06/15/2022 - 06/19/2022: 111 records
• 06/19/2022 - 06/22/2022: 130 records
• 06/22/2022 - 06/26/2022: 207 records
• 06/26/2022 - 06/29/2022: 321 records
• 06/29/2022 - 07/03/2022: 523 records
• 07/03/2022 - 07/07/2022: 924 records
• 07/07/2022 - 07/10/2022: 861 records The dataset features 19 unique countries and 1347 unique video IDs. View counts for videos in the dataset range from approximately 20.9 thousand to 123 million.

Usage

• Exploratory Data Analysis (EDA): Discovering patterns, anomalies, and relationships within YouTube trending content.
• Data Manipulation and Querying: Practising data handling using libraries such as Pandas or Numpy in Python, or executing queries with SQL.
• Natural Language Processing (NLP): Analysing video titles, tags, and descriptions to extract key themes, sentiment, and trending topics.
• Trend Prediction: Developing models to forecast future trending videos or content categories.
• Cross-Country Comparison: Examining how trending content varies across different Mediterranean nations.

Coverage

• Geographic Scope: The dataset covers YouTube trending video statistics for 19 specific Mediterranean countries. These include Italy (IT), Spain (ES), Greece (GR), Croatia (HR), Turkey (TR), Albania (AL), Algeria (DZ), Egypt (EG), Libya (LY), Tunisia (TN), Morocco (MA), Israel (IL), Montenegro (ME), Lebanon (LB), France (FR), Bosnia and Herzegovina (BA), Malta (MT), Slovenia (SI), Cyprus (CY), and Syria (SY).
• Time Range: The data primarily spans from 2022-06-04 to 2022-07-10, providing detailed daily trending information. A specific snapshot of the dataset is also available for 2022-11-07.

License

Who Can Use It

• Data Scientists and Analysts: For conducting in-depth research, building predictive models, and generating insights on social media trends.
• Researchers: Those studying online content consumption patterns, regional cultural influences, and digital media behaviour.
• Marketing Professionals: To identify popular content types, inform content strategy, and understand audience engagement on YouTube.
• Students: For academic projects focusing on web data analysis, natural language processing, and statistical modelling.

Dataset Name Suggestions

• Mediterranean YouTube Trends 2022
• YouTube Trending Videos: Mediterranean Insights
• Regional YouTube Viral Content
• Mediterranean Social Media Video Data
• YouTube Trends in Southern Europe & North Africa

Attributes