---
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)

---