matesanz/fake-news-detector

We'll train a model on a kaggle dataset to detect fake news.

Fake News Detection using Natural Language Processing (NLP)

Table of Contents

• Fake News Detection using Natural Language Processing (NLP)
  • Table of Contents
  • Introduction
  • Project Overview
  • Installation
  • Usage
  • Data
  • Algorithm
  • Evaluation
  • Contributing
  • License

Introduction

This project aims to detect fake news using Natural Language Processing (NLP) techniques. Fake news is a pressing issue in today's digital age, where misinformation can spread rapidly and have serious consequences. By leveraging NLP, we can develop a system that can help identify potentially misleading or false information in news articles and social media content.

Project Overview

The project consists of several components:

1. Data Collection: We gather a dataset of news articles, including both real and fake news, to train and test our model.

2. Preprocessing: We preprocess the text data by removing stopwords, tokenizing, and performing other text cleaning operations.

3. Feature Extraction: We extract relevant features from the text data, such as TF-IDF (Term Frequency-Inverse Document Frequency) and word embeddings.

4. Model Development: We build and train an NLP model using various machine learning or deep learning techniques to classify news articles as either real or fake.

5. Evaluation: We evaluate the model's performance using metrics like accuracy, precision, recall, and F1-score.

6. Web Application: Optionally, we can create a web application or API for users to input news articles and receive a fake news detection result.

Installation

This project uses

• numpy
• scikit-learn
• matplotlib

You can install these libraries using pip:

pip install .

You can also install them using poetry:

poetry install

Usage

1. Clone this repository:

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

2. Navigate to the project directory:

cd fake-news-detection

3. Follow the instructions in the project's README to run the code, preprocess the data, train the model, and evaluate its performance.

Data

The dataset used for this project is a crucial component. It typically includes a collection of news articles labeled as real or fake. You can find such datasets on platforms like Kaggle, or you can curate your own. Make sure you have a training and testing dataset with ground truth labels for your model.

Algorithm

The choice of algorithm depends on your project requirements and dataset. Common algorithms for text classification tasks like fake news detection include:

• Multinomial Naive Bayes
• Logistic Regression
• Random Forest
• Recurrent Neural Networks (RNNs)
• Convolutional Neural Networks (CNNs)
• BERT (Bidirectional Encoder Representations from Transformers)

You can experiment with different algorithms and fine-tune hyperparameters to achieve the best results.

Evaluation

The model's performance is assessed through various metrics, including:

• Accuracy: The proportion of correctly classified articles.
• Precision: The ratio of true positive predictions to the total positive predictions.
• Recall: The ratio of true positive predictions to the total actual positive instances.
• F1-score: The harmonic mean of precision and recall.

These metrics provide insights into the model's ability to classify real and fake news accurately.

Contributing

Contributions are welcome! If you want to contribute to the project, please follow the guidelines in the project's CONTRIBUTING.md file.

License

This project is licensed under the MIT License. Feel free to use, modify, and distribute it according to the terms of the license.

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Feel free to reach out to us if you have any questions or want to collaborate on improving fake news detection using NLP. Together, we can work toward a more reliable and trustworthy information ecosystem.