Fake News & Misinformation Detector

Detect fake vs real news articles using Machine Learning, TF-IDF, and Logistic Regression, complete with training scripts, evaluation charts, and an interactive Streamlit web app.

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Table of Contents

• Overview
• Demo
• Project Structure
• Installation
• Dataset
• Training the Model
• Evaluation & Charts
• How It Works
• Running the Streamlit App
• Code Modules
• Technologies Used
• License
• Author
• Future Improvements

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Overview

The Fake News & Misinformation Detector is a complete end-to-end Natural Language Processing (NLP) project that classifies news headlines and articles as REAL or FAKE.
It combines TF-IDF feature extraction with a Logistic Regression classifier, achieving perfect accuracy on the cleaned dataset.

The project also includes:

• Model evaluation with visual charts
• Interactive Streamlit web app
• Reusable and modular code structure

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Demo

Streamlit Web App

When launched, the app allows you to paste or type any news headline or paragraph and analyze its credibility in real time.

• Prediction: REAL or FAKE
• Probability bar visualization
• Adjustable fake-detection threshold

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Project Structure

fake-news-detector/
│
├── data/
│   ├── True.csv                 # Real news (999 rows)
│   ├── Fake.csv                 # Fake news (999 rows)
│
├── outputs/
│   ├── model.joblib             # Trained Logistic Regression model
│   ├── vectorizer.joblib        # TF-IDF vectorizer
│   ├── pipeline.joblib          # Combined pipeline (optional)
│   ├── metrics.json             # Model performance report
│   ├── confusion_matrix.png     # Confusion Matrix plot
│   ├── roc_curve.png            # ROC curve plot
│   └── pr_curve.png             # Precision-Recall curve plot
│
├── src/
│   ├── text_clean.py            # Text preprocessing utilities
│   ├── utils.py                 # I/O helpers
│   ├── train_model.py           # Training and evaluation script
│   ├── detect_fake_news.py      # CLI prediction script
│   └── streamlit_app.py         # Streamlit web application
│
└── README.md

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Installation

Clone the Repository

git clone https://github.com/yourusername/fake-news-detector.git
cd fake-news-detector

Install Dependencies

pip install -r requirements.txt

Or install manually:

pip install pandas numpy scikit-learn matplotlib streamlit joblib

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Dataset

File	Type	Rows	Columns
True.csv	Real news	999	title, text, subject, date
Fake.csv	Fake news	999	title, text, subject, date

Dataset Source:
This project uses and modifies the Fake and Real News Dataset by Clément Bisaillon (Kaggle).
Data was cleaned, header-fixed, and downsampled to 999 REAL and 999 FAKE news articles for balanced training and clear visualization.
Used purely for educational and research purposes.

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Training the Model

Run the following command from the project root:

python src/train_model.py --real data/True.csv --fake data/Fake.csv --text-col text --outdir outputs

This script will:

1. Load both datasets (real and fake).
2. Clean and merge them using text_clean.py.
3. Extract TF-IDF features.
4. Train a Logistic Regression classifier.
5. Save outputs:
   • outputs/model.joblib
   • outputs/vectorizer.joblib
   • outputs/metrics.json
   • Performance charts (confusion_matrix.png, roc_curve.png, pr_curve.png)

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Evaluation & Charts

After training, the model achieves perfect classification accuracy on this dataset.

Confusion Matrix

True Label	Predicted REAL	Predicted FAKE
REAL	999 ✅	0 ❌
FAKE	0 ❌	999 ✅

The model correctly classified all 1,998 samples.

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ROC Curve

The ROC curve touches the top-left corner AUC = 1.00
Perfect separability between classes.

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Precision–Recall Curve

Both precision and recall reach 1.00, meaning zero false predictions.

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Key Metrics

Metric	Value
Accuracy	100 %
Precision (FAKE)	1.00
Recall (FAKE)	1.00
F1-Score	1.00
ROC-AUC	1.00

Although perfect accuracy is achieved on this dataset, it’s a controlled sample. Real-world news data will naturally introduce noise and uncertainty.

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How It Works

Pipeline Overview

1. Text Cleaning → Remove punctuation, URLs, emails, non-ASCII chars.
2. TF-IDF Vectorization → Convert words into weighted numerical features.
3. Logistic Regression → Predict probability of “FAKE” label.
4. Thresholding → If p(fake) ≥ 0.5 → FAKE, else REAL.

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Example: Command-Line Prediction

python src/detect_fake_news.py --model outputs/model.joblib   --vectorizer outputs/vectorizer.joblib   --text "It s tough sometimes to imagine that Donald Trump has five children since it s clear from Monday s speech in front of 40,000 Boy Scouts and other attendees at the Boy Scouts Jamboree in West Virginia that he has absolutely no idea what kind of talk is appropriate for children.While most adults would take this opportunity to offer some pearls of adult wisdom or cheerlead the Boy Scouts toward their futures, Trump chose to deliver a tirade of Trumpisms.Like almost any time Trump has tried to string together more than a couple of words at a time, most of his speech was an inarticulate mess which consisted of his trademark whining, a wee bit of swearing and a pointless anecdote about a burned out rich guy at a cocktail party."

Output:

Label: FAKE | Fake probability: 0.560 | Threshold: 0.40

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Running the Streamlit App

Launch the App

streamlit run src/streamlit_app.py

Then open the local web interface:

http://localhost:8501

App Features

• Paste any headline or paragraph
• Analyze with one click
• Adjust FAKE probability threshold
• See model file locations and loaded status in sidebar

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Code Modules

Module	Purpose
text_clean.py	Handles text normalization (lowercasing, regex-based cleaning)
utils.py	Ensures output directories exist and handles JSON I/O
train_model.py	Loads data, trains the model, and generates metrics and plots
detect_fake_news.py	CLI script for predicting individual samples
streamlit_app.py	Streamlit web app for interactive user testing

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Technologies Used

• Python 3.10+
• scikit-learn → TF-IDF Vectorizer, Logistic Regression
• pandas / numpy → Data manipulation
• matplotlib → Model visualization
• joblib → Model persistence
• Streamlit → Web interface

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Future Improvements

• Integrate BERT / DistilBERT for contextual language understanding
• Extend dataset for multi-language fake news detection
• Add Explainable AI (LIME / SHAP) for model transparency
• Deploy live on Streamlit Cloud or Hugging Face Spaces