Public Tweet Analysis Dataset

Columns

• Tweet Id: A unique identifier for each individual tweet.
• Tweet URL: The direct web address pointing to the tweet on Twitter.
• Tweet Posted Time (UTC): The timestamp indicating when the tweet was published, specified in Coordinated Universal Time. The dates range from 1 July 2019 to 30 August 2019.
• Tweet Content: The actual text and any associated media links or hashtags within the tweet.
• Tweet Type: Categorises the tweet, predominantly as "ReTweet" (72%) or "Tweet" (25%).
• Client: Identifies the application or platform used to post the tweet, such as "Twitter for iPhone" (45%) or "Twitter for Android" (16%).
• Retweets Received: The total count of times the tweet was retweeted by other users.
• Likes Received: The total count of 'likes' or 'favourites' the tweet accumulated.
• Tweet Location: The geographical location from which the tweet was posted, with "Brussels" being a common entry among the 105 unique locations. Approximately 16% of these values are missing.
• Tweet Language: The detected language of the tweet, with English accounting for 98% of the entries.
• User Id: A unique identifier assigned to the Twitter user who posted the tweet.
• Name: The display name of the Twitter user.
• Username: The user's unique Twitter handle (e.g., @username).
• User Bio: The short descriptive text from the user's Twitter profile.
• Verified or Non-Verified: Indicates whether the user's Twitter account is officially verified.
• Profile URL: The direct link to the user's Twitter profile page.
• Protected or Non-protected: States whether the user's Twitter account is set to private.
• User Followers: The number of followers the user has on Twitter.
• User Following: The number of accounts the user is following on Twitter.
• User Account Creation Date: The date when the user's Twitter account was established.
• Impressions: The total number of times the tweet was viewed.

Distribution

Usage

• Social Media Analysis: Investigating trends, popular topics, and user engagement patterns on Twitter.
• Text Mining and Natural Language Processing (NLP): Developing and testing algorithms for sentiment analysis, topic modelling, and content categorisation.
• Data Cleaning Exercises: Practising data preparation techniques, especially for unstructured text data.
• Understanding User Behaviour: Examining how different factors, such as tweet content or user characteristics, correlate with engagement metrics like retweets and likes.

Coverage

License

Who Can Use It

• Researchers: To conduct studies on social media dynamics, public opinion, and communication patterns.
• Data Scientists and Analysts: For developing and refining models for social media content analysis, engagement prediction, and user segmentation.
• Students and Educators: As a practical dataset for learning about data collection, cleaning, and analysis in the context of social media.
• Developers: To prototype applications that process or visualise real-time social media data.

Dataset Name Suggestions

• Twitter Sample Tweets (July-August 2019)
• Social Media Activity Snapshot 2019
• Public Twitter Data (Jul-Aug 2019)
• Historical Tweet Interactions Dataset

Attributes