Deep Learning Analysis for Revealing Fake News using Linguistic Complexity and Semantic Signatures.
Keywords:
Fake news, BERT, Attention mechanism, False InformationAbstract
Despite the fact that there is an abundance of information in the modern world, the widespread impact of false information necessitates the utilization of sophisticated methods in order to recognize and uncover it. This research paper addresses the growing problem of spreading fake news by utilizing a sophisticated approach that integrates state-of-the-art deep learning techniques, analysis of linguistic complexity, and advanced natural language processing (NLP) methods. The paper explores the selection and thorough analysis of four cutting-edge deep learning architectures: the LSTM-Attention Mechanism, BERT, and GPT. The architecture, training process, and key parameters of each method are carefully examined, emphasizing their unique advantages and drawbacks. Simultaneously, linguistic complexity metrics offer a detailed examination of the intricacies of the text, while NLP techniques like Word Embeddings (e.g., Word2Vec) and Named Entity Recognition (NER) help uncover semantic patterns within the text. The research examines not only the individual capabilities of these techniques but also explores their potential for collaboration. The thorough analysis and interpretation of results provide deep insights into the intricate terrain of misinformation detection. The combination of deep learning, linguistic complexity, and semantic signatures is a key factor that shows promise in improving the accuracy and flexibility of fake news detection mechanisms. The findings offer significant insights for researchers, practitioners, and policymakers involved in the ongoing endeavor to address the widespread dissemination of false information in modern information ecosystems.
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