license: apache-2.0
language:
- en
tags:
- social
- analytic
- x-analytics
- engagement-prediction
- twitter
pretty_name: The AI Thread Engagement Predictor
size_categories:
- n<1K
datasets:
- ai-thread-engagement-rate
AI Thread Engagement Rate Predictor Dataset
This dataset contains a real-world, manually collected sample of 14 threads posted on X (formerly Twitter) under this account between September 2024 and January 2025.
Despite its small size, it is an authentic dataset with real engagement metrics, making it ideal for small-scale experiments, educational purposes, and exploratory analysis of how post features influence engagement.
📌 Purpose
The dataset is designed to help answer:
Can we predict a thread's engagement rate based on its content, structure, and other posting attributes?
Engagement Rate is defined by X as:
The total number of times a user has interacted with a post. This includes all clicks (hashtags, links, usernames, post expansions), reposts, replies, follows, and likes.
🛠️ Collection Methodology
Data Source:
Metrics were collected using X Post Analytics, tracking user engagement, impressions, and other relevant metrics.Readability Analysis:
Grammarly's data was used to compute the Flesch Reading Ease score and other textual analysis metrics.
📊 Features Captured
The dataset includes the following columns:
| Column | Description |
|---|---|
| id | Unique identifier for each thread |
| word_count | Total number of words in each thread |
| reading_time(s) | Estimated reading time (in seconds) |
| readability_score | Flesch Reading Ease score (higher = easier to read) |
| posts_per_thread | Number of posts within each thread |
| topic_complexity | Subjective rating of the thread’s topic complexity |
| media_count | Number of media elements (images, videos, quizzes) per thread |
| posting_time | Time when the thread was posted (in IST) |
| post_frequency | Number of posts made by the account in a week |
| impressions | Number of times the thread was viewed |
| emojis | Number of emojis used within the thread |
| engagements | Total user engagements (likes, comments, reposts, follows, etc.) |
CSV Header Row: id word_count reading_time(s) readability_score posts_per_thread topic_complexity media_count posting_time post_frequency impressions emojis engagements
🔄 Data Cleaning & Transformation
- Basic data cleaning steps were applied.
- Consistency checks ensured no missing or corrupted values.
- Readability scores were normalized, numeric features standardized where necessary.
📓 Additional Resources
A Jupyter Notebook is available demonstrating:
- Exploratory data analysis (EDA)
- A simple neural network model built to predict engagement rate.
🔍 Potential Use Cases
- Investigate the relationship between post characteristics (e.g., content length, readability, media usage) and engagement.
- Build machine learning models to predict engagement rate.
- Study how readability, timing, and media inclusion affect post performance.
- Experiment with small, real-world datasets for educational purposes.
📄 License
- License: Apache 2.0
- Usage: Publicly available for research and educational purposes.
- Commercial Use: Not permitted unless explicitly allowed under the license terms.
📢 Source
- Data Source: X Analytics
- Account: PulkitSahu89