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📰 Fake News Detection Using Deep Learning Methods

NEUROMATCH 2024 - NATURAL LANGUAGE PROCESSING

A Novel Approach for Detecting Fake News using Deep Learning Methods

Project Overview

In this notebook, we propose several embedding and model pairs to classify news articles as either real or fake. The following approaches were explored:

  1. GloVe Embeddings + LSTM (both uni/bidirectional models)
  2. TFIDF + Logistic Regression
  3. CountVectorizer + Logistic Regression
  4. Pretrained Tokenizer + Transformer Model from BERT

Installation and Setup

To run this project, you'll need to install the required libraries. Use the following commands to set up your environment:

pip install numpy pandas matplotlib seaborn torch torchtext scikit-learn tqdm

Usage

Using preprocessing notebook, you can download the original dataset and apply whichever preprocessing steps you'd like.

Import the Dataset

You can either import the preprocessed data (news_df_processed.csv) or the raw dataframe that is of the form {'label': (0 or 1), 'content': (article string)}.

news_df = pd.read_csv('path/to/your/news_df_processed.csv')

GPU Limitations

If you are going to run the notebook on base version GPU's, we highly recommend keeping embedding_dimensions under 250. Otherwise your session might crash and you'd lose all your progress.

Training BERT Model

Beware of the fact that each training epoch takes 10 mins on 15.84 GB Tesla T4 GPU.

history = model.fit(x = {'input_ids':X_train_token['input_ids'],'input_mask':X_train_token['attention_mask']}, y = Y_train, epochs=2, validation_split = 0.2, batch_size = 30, callbacks=[callback])

Acknowledgements

I would like to thank the Neuromatch Academy for providing the platform and resources for this project.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any questions or suggestions, please contact:

Boran Aybak Kilic boranaybak34@gmail.com