id
int64 1
14
| word_count
int64 139
497
| reading_time(s)
int64 33
120
| readability_score
int64 39
86
| posts_per_thread
int64 2
7
| topic_complexity
int64 1
3
| media_count
int64 0
3
| posting_time
float64 11
12.5
| post_frequency
int64 1
3
| impressions
int64 256
4.99k
| emojis
int64 0
6
| engagements
int64 42
745
|
|---|---|---|---|---|---|---|---|---|---|---|---|
1
| 497
| 120
| 62
| 7
| 1
| 3
| 11
| 3
| 4,988
| 3
| 745
|
2
| 356
| 85
| 62
| 5
| 1
| 3
| 11
| 3
| 884
| 6
| 150
|
3
| 156
| 37
| 77
| 3
| 1
| 2
| 11
| 3
| 773
| 0
| 124
|
4
| 319
| 55
| 76
| 3
| 2
| 2
| 11
| 3
| 561
| 3
| 113
|
5
| 432
| 103
| 61
| 5
| 2
| 3
| 11
| 3
| 523
| 0
| 78
|
6
| 164
| 39
| 76
| 3
| 2
| 2
| 11
| 3
| 504
| 0
| 87
|
7
| 225
| 53
| 60
| 2
| 1
| 1
| 11
| 3
| 256
| 0
| 42
|
8
| 253
| 60
| 55
| 3
| 1
| 1
| 11
| 3
| 370
| 0
| 58
|
9
| 139
| 33
| 61
| 3
| 1
| 1
| 11
| 3
| 330
| 3
| 58
|
10
| 210
| 50
| 39
| 3
| 1
| 0
| 11
| 1
| 313
| 0
| 50
|
11
| 467
| 112
| 59
| 4
| 3
| 1
| 12
| 1
| 662
| 0
| 53
|
12
| 388
| 93
| 60
| 3
| 3
| 2
| 12.5
| 1
| 480
| 0
| 72
|
13
| 363
| 87
| 69
| 4
| 3
| 1
| 11
| 1
| 732
| 0
| 85
|
14
| 380
| 91
| 86
| 4
| 3
| 1
| 11
| 1
| 567
| 0
| 76
|
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
- Downloads last month
- 18