Enhancing the Identification of False News using Machine Learning Algorithms: A Comparative Study
DOI:
https://doi.org/10.56294/mr202466Keywords:
Machine Learning, Natural Language Processing, Feature Engineering, Deep Learning, Data Analytics, Algorithm AdaptationAbstract
In today's digital world filled with information overload, preventing the rampant spread of fake news has become an urgent task. Discover the Fake News Prediction System (FNPS), which uses advanced machine learning and technology to provide innovative solutions and powerful methodologies. Natural language processing methods. FNPS uses sophisticated feature engineering from diverse, curated datasets to identify underlying patterns in fraudulent content and significantly improves the ability to recognize authenticity. FNPS achieves outstanding performance using a combination of classifiers combining TF-IDF vectorization, deep learning architecture, and sentiment analysis, demonstrating its ability to accurately predict the legitimacy of news articles. Beyond simple forecasting, FNPS provides an intuitive user interface for evaluating news content in real time. This not only increases accessibility but also promotes media literacy and responsible consumption of information. Provides additional information and promotes robust public discourse. FNPS essentially demonstrates the revolutionary potential of advanced technology in ongoing combat. This will further the important public goal of ensuring the reliability and integrity of information in the digital age.
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Copyright (c) 2024 Patakamudi Swathi, Dara Sai Tejaswi, Mohammad Amanulla Khan, Miriyala Saishree, Venu Babu Rachapudi, Dinesh Kumar Anguraj (Author)
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