Published Paper Details:

With the rapid growth of information on social media and news platforms, the spread of fake news has become a significant concern. Fake news can manipulate public opinion, disrupt social order, and negatively impact decision-making. This paper explores the application of Natural Language Processing (NLP) techniques for detecting fake news, focusing on the BERT (Bidirectional Encoder Representations from Transformers) algorithm. BERT, a state-of-the-art pre-trained language model, enables deep understanding of the context within text by processing words in relation to all other words in a sentence. We propose a framework that leverages BERT's powerful contextual embeddings for classifying news articles as either real or fake. The model is fine-tuned on a large dataset of labeled news articles, which enhances its ability to distinguish between truthful and deceptive content. Results show that BERT-based models outperform traditional machine learning methods in terms of accuracy, precision, and recall. This study highlights the potential of using BERT in fake news detection and offers insights into how NLP can be used to combat misinformation in the digital age.