Study of Fake News Detection for Twitter Data using Deep Learning Approach
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Abstract
The exponential growth of social media platforms like Twitter has made information dissemination faster and more accessible, but it has also given rise to the rapid spread of fake news and misinformation. Detecting fake news in real-time is a challenging task due to the dynamic nature of social media content and the complexity of natural language. This study explores the application of deep learning techniques for fake news detection using Twitter data. Various deep neural architectures, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional Neural Networks (CNN), and hybrid models such as CNN-LSTM, are analyzed for their efficiency in classifying tweets as genuine or fake. The dataset is preprocessed through tokenization, stop-word removal, and word embeddings using Word2Vec and GloVe. The models are trained and validated using evaluation metrics such as accuracy, precision, recall, F1-score, and AUC. Experimental results demonstrate that deep learning models outperform traditional machine learning classifiers in understanding contextual semantics and sentiment patterns within tweets. The study concludes that hybrid deep learning models, particularly BiLSTM-CNN architectures, provide superior performance and robustness in identifying misinformation on social media.
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