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Tweet Thread Dynamics Dataset
Social Media and Networking
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Free
About
This dataset provides details on Twitter threads, focusing on the engagement dynamics of individual tweets within a thread. It was compiled to explore the observed phenomenon where engagement metrics such as retweets, likes, and replies typically decrease with each subsequent tweet in a thread. The data offers insights into how users interact with multi-tweet content and can be used to analyse factors influencing engagement, potentially aiding in the development of strategies for optimising content on the platform. It also offers scope for Natural Language Processing (NLP) to understand how the context of a thread might affect engagement patterns.
Columns
- id: A unique identifier for each tweet.
- thread_number: An identifier used to group individual tweets belonging to the same thread.
- timestamp: The creation date and time of each tweet.
- text: The actual content of each tweet.
- retweets: The number of times each tweet was retweeted.
- likes: The number of times each tweet was liked.
- replies: The number of replies each tweet received.
Distribution
The dataset is organised into five distinct files, categorised by thread length: those with 5-10 tweets, 10-15 tweets, 15-20 tweets, 20-25 tweets, and 25-30 tweets. Each of these categories, or "bins," contains approximately 100 unique threads, resulting in around 500 threads in total. All files maintain an identical column structure. The dataset includes a substantial number of individual tweet records, with counts for different metrics like retweets and likes extending into the thousands across various value ranges. For example, there are 1,732 records with 0-63.4 likes and 1,695 records with 0-2026.7 retweets.
Usage
This dataset is ideal for:
- Analysing engagement patterns within social media threads.
- Conducting social science research on online communication behaviour.
- Developing and testing hypotheses regarding content effectiveness on platforms like Twitter.
- Exploring the influence of tweet content and context on user interaction using NLP techniques.
- Informing content strategy and optimisation for social media managers and marketers.
Coverage
The dataset consists of tweets collected between October 2017 and May 2018. The data is global in scope, reflecting general Twitter activity. While no specific demographics are detailed, observations from the data collection suggest that the context or topic of threads (e.g., political vs. art threads) may influence engagement. The threads included were chosen solely based on their length, ranging from 5 to 30 tweets, irrespective of their content.
License
CC0
Who Can Use It
This dataset is suitable for:
- Social media researchers and academics investigating online engagement and communication.
- Data scientists and analysts performing quantitative analysis on social media data.
- Marketing professionals seeking to understand and improve their social media content performance.
- Natural Language Processing (NLP) practitioners interested in text analysis within a conversational context.
- Students learning about data analysis and social media trends.
Dataset Name Suggestions
- Twitter Thread Engagement Analysis
- Social Media Thread Interaction Data
- Tweet Thread Dynamics Dataset
- Twitter Engagement Study
Attributes
Original Data Source: Twitter Threads
QUALITY