Deep Learning Systems for Fake News Detection

Title: Fake News Detection Using Deep Learning (LSTM)

Description:

I have completed a project on Fake News Detection using Deep Learning (LSTM) as part of my Data Science & AI training at Diginique TechLab.

The project focused on building an intelligent system capable of distinguishing between real and fake news articles using Natural Language Processing (NLP) and advanced deep learning techniques.

Key Highlights:

Utilized ISOT Fake News Dataset (44,898 records including real & fake articles).

Applied data preprocessing & cleaning (stop word removal, tokenization, and regex-based text cleaning).

Built word embeddings (GloVe) to capture semantic relationships in text.

Designed and trained a bidirectional LSTM model using TensorFlow/Keras.

Achieved 98.3% test accuracy with an F1-score of 0.98.

Implemented performance evaluation with the confusion matrix, ROC-AUC curve, and precision-recall metrics.

Proposed integration as a web app or Chrome extension for real-time fake news detection.

Skills Used:

Python (NumPy, Pandas, Matplotlib, Seaborn)

Machine Learning & Deep Learning (TensorFlow, Keras, LSTM, BERT)

NLP (Text Preprocessing, Tokenization, Word Embeddings)

Data Analysis & Visualization

Research & Report Writing

GitHub Repository:

Diginique-TechLab-Project (GitHub)