Leveraging N-gram Features and LSTM Networks for Enhanced Fake News Detection

This paper presents a comprehensive analysis of fake news, examining its proliferation in the digital age, detection methodologies, and impact on society. Through an exploration of current research and emerging technologies, we investigate the challenges and solutions in combating misinformation. This proposed methodology aims to develop an effective method for detecting fake news using an N-Gram feature selection technique combined with a Long Short-Term Memory (LSTM) model. The N-Gram approach is used to capture textual patterns and features that are key in identifying misleading or fabricated information. The model combines natural language processing techniques with deep learning, utilizing N-grams (unigrams, bigrams, trigrams) as features to capture contextual information. The work involves data collection, preprocessing (tokenization, normalization), N-gram creation, and feature selection (Term Frequency-Inverse Document Frequency, Chi-Squared tests). By leveraging linguistic features and deep learning methodologies, this approach enhances news classification accuracy, providing a powerful tool in the fight against misinformation. This approach not only enhances fake news detection accuracy but also provides a scalable solution for real-time misinformation detection, enabling timely responses to counter false information, helping to maintain the integrity of information shared across online platforms.