Automatic Fake News Spreader Profiling Detecting

This repository contains code and resources for a machine learning framework developed for the detection of fake news posts on the online social media platform Twitter. We have conducted classification experiments on both an English and a Spanish dataset to achieve reliable results.

Our approach involved testing various methods and combinations to identify the best performing model for detecting fake news tweets. After careful evaluation, we found that the Gradient Boosting & Random Forest model, combined with features extracted from a TF-IDF approach, yielded the most accurate results.

For the English dataset, our model achieved an accuracy score of 75.33% mean accuracy in detecting fake news tweets. Similarly, for the Spanish dataset, we achieved a mean accuracy of 77.66%.

• data/: This directory contains the English and Spanish datasets used for training and testing.
• code/: This directory contains the Python scripts used to preprocess the data, train the models, and evaluate the performance.
• models/: This directory contains the trained models saved in the pickle format.
• results/: This directory contains the evaluation results and performance metrics of the models.

1. Clone this repository:

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