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NLP Fake News Classifier Data
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About
This synthetic dataset is designed for practicing fake news detection using natural language processing (NLP) techniques. It contains 1000 news samples labeled as "real" or "fake", including fabricated headlines and articles that mimic real-world patterns. Researchers and students can utilise this dataset to train NLP classification models, perform feature engineering on textual data, and practice binary classification problems in news analytics.
Columns
- title: The news headline.
- text: The main body of the news article.
- label: A label indicating whether the news is "fake" or "real".
Distribution
The dataset comprises 1000 news samples. The data file is typically in CSV format, and sample files will be updated separately to the platform.
Usage
Ideal applications include:
- Training NLP classification models such as Logistic Regression, SVM, and BERT.
- Performing feature engineering on textual data.
- Practicing binary classification problems in the context of news analytics.
Coverage
The dataset's geographic scope is global. It was listed on 5th June 2025, providing data for general news pattern analysis.
License
CCO
Who Can Use It
Intended users include:
- Researchers interested in natural language processing and machine learning applications.
- Students learning about natural language processing, text classification, and data science.
- Anyone aiming to develop or test models for fake news detection.
Dataset Name Suggestions
- Fake News Detection Dataset
- NLP Fake News Classifier Data
- News Authenticity Data
- Synthetic News Classification Data
- Real and Fabricated News Samples
Attributes
Original Data Source: Fake News Detection
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