qg2020252627/twitter_author_profiling_by_gender_nlp

This dataset was created for a student's Bc work. The main purpose for which the dataset was created is to use it in author profiling by gender.

Single-Tweet-Per-Author Twitter Dataset

Overview

This dataset consists of Twitter (X) posts with a strict constraint: each author appears exactly once.
There is a one-to-one correspondence between tweets and authors.

This design removes author-level accumulation effects and prevents models from exploiting repeated stylistic or behavioral signals from the same individual.

Key Property

• 1 tweet = 1 unique author
• No author_id is repeated
• Number of tweets equals number of authors

Intended Use

The dataset is intended for:

• Text classification
• Sentiment analysis
• Topic classification
• Bias and fairness analysis
• Modeling tasks requiring independent textual observations

It is explicitly designed to avoid author leakage.

Not Intended Use

The dataset should not be used for:

• Author identification or profiling
• Longitudinal analysis
• User behavior modeling
• Style consistency analysis

Dataset Structure

Each record represents a single tweet from a single author.

Example Record

{
  "tweet_id": "1234567890",
  "gender": "M|F",
  "text": "Example tweet text",
}