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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- social |
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- analytic |
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- x-analytics |
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- engagement-prediction |
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- twitter |
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pretty_name: The AI Thread Engagement Predictor |
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size_categories: |
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- n<1K |
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datasets: |
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- ai-thread-engagement-rate |
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--- |
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# AI Thread Engagement Rate Predictor Dataset |
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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**. |
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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. |
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--- |
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## 📌 Purpose |
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The dataset is designed to help answer: |
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**Can we predict a thread's engagement rate based on its content, structure, and other posting attributes?** |
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**Engagement Rate** is defined by X as: |
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> 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. |
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--- |
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## 🛠️ Collection Methodology |
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- **Data Source:** |
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Metrics were collected using **X Post Analytics**, tracking user engagement, impressions, and other relevant metrics. |
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- **Readability Analysis:** |
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**Grammarly's data** was used to compute the Flesch Reading Ease score and other textual analysis metrics. |
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--- |
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## 📊 Features Captured |
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The dataset includes the following columns: |
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| Column | Description | |
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|----------------------|------------------------------------------------------------------------------| |
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| **id** | Unique identifier for each thread | |
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| **word_count** | Total number of words in each thread | |
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| **reading_time(s)** | Estimated reading time (in seconds) | |
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| **readability_score** | Flesch Reading Ease score (higher = easier to read) | |
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| **posts_per_thread** | Number of posts within each thread | |
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| **topic_complexity** | Subjective rating of the thread’s topic complexity | |
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| **media_count** | Number of media elements (images, videos, quizzes) per thread | |
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| **posting_time** | Time when the thread was posted (in IST) | |
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| **post_frequency** | Number of posts made by the account in a week | |
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| **impressions** | Number of times the thread was viewed | |
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| **emojis** | Number of emojis used within the thread | |
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| **engagements** | Total user engagements (likes, comments, reposts, follows, etc.) | |
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**CSV Header Row:** |
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id word_count reading_time(s) readability_score posts_per_thread topic_complexity media_count posting_time post_frequency impressions emojis engagements |
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--- |
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## 🔄 Data Cleaning & Transformation |
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- Basic data cleaning steps were applied. |
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- Consistency checks ensured no missing or corrupted values. |
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- Readability scores were normalized, numeric features standardized where necessary. |
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--- |
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## 📓 Additional Resources |
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A **Jupyter Notebook** is available demonstrating: |
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- Exploratory data analysis (EDA) |
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- A simple neural network model built to predict engagement rate. |
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👉 **[Kaggle Notebook Link](https://www.kaggle.com/code/pulkitsahu89/simple-neural-network)** |
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## 🔍 Potential Use Cases |
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- Investigate the relationship between post characteristics (e.g., content length, readability, media usage) and engagement. |
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- Build machine learning models to predict engagement rate. |
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- Study how readability, timing, and media inclusion affect post performance. |
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- Experiment with small, real-world datasets for educational purposes. |
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--- |
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## 📄 License |
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- **License:** Apache 2.0 |
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- **Usage:** Publicly available for research and educational purposes. |
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- **Commercial Use:** Not permitted unless explicitly allowed under the license terms. |
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--- |
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## 📢 Source |
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- **Data Source:** X Analytics |
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- **Account:** [PulkitSahu89](https://x.com/PulkitSahu89) |
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