Fake News Detection Using Natural Language Processing
Authors:-Professor Kirti Randhe, Nameet Vyavahare, Rajkumar Vishwakarma, Sai Kudale
Abstract-:In the digital age, the rapid dispersal of information through social media and online platforms has increased the spread of fake and exaggerated news, posing serious challenges and threats to public trust, societal stability, democratic processes and national security and peace. This research explores the application of Natural Language Processing (NLP) techniques for the automatic detection of fake news, aiming to enhance the reliability of information consumed by the public. By leveraging and applying machine learning and deep learning models in conjunction with NLP methods such as text preprocessing, tokenization, feature extraction, and sentiment analysis, this study investigates effective strategies for distinguishing between factual, genuine and misleading content. Various algorithms, including Support Vector Machines, Random Forest, Naïve Bayes and deep learning approaches like LSTM and BERT, are evaluated using benchmark datasets. The results demonstrate the potential of NLP-driven solutions to accurately classify news articles, highlighting their significance in combating misinformation. This paper contributes to the growing field of automated fake news detection and offers insights into building more trustworthy digital information ecosystems.
