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.

👉 Kaggle Notebook Link

🔍 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