Fake News Classification using BLSTM with Glove Embedding

Abstract

Considering the extensive utilization of social media platforms, false information is more likely to spread rapidly among users. With the surge of user-generated content, a significant amount of the information being shared is in-accurate and has no connection to reality. It can be chal-lenging to automatically identify and verify the accuracy of textual content. This research project aims to employ B-LSTM deep learning model with dynamic and static pre-trained embeddings to classify news datasets utilizing three fake news benchmarking datasets.

More by Selcuk Cankurt

• Effects of Binary Similarity Metrics on Energy Consumption During Recommendation by Using Collaborative Filtering ApproachJournal of sustainable development issues. · 2026
• STUDENT PERFORMANCE PREDICTION USING MACHINE LEARNINGZenodo · 2026
• Interpretable Hybrid CNN-BiLSTM-Attention Model for ECG Arrhythmia ClassificationIEEE International Conference on Smart Information Systems and Technologies · 2026
• COLLABORATIVE MULTI-AGENT CHATBOT FRAMEWORK FOR INTELLIGENT TUTORING AND FEEDBACKZenodo · 2026