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---
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](https://x.com/PulkitSahu89/status/1833014886776832314) 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.
👉 **[Kaggle Notebook Link](https://www.kaggle.com/code/pulkitsahu89/simple-neural-network)**
---
## 🔍 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](https://x.com/PulkitSahu89)
---
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