The proliferation of fake news in the digital domain poses a significant threat to public discourse, necessitating the
development of effective detection mechanisms. Therefore, this paper presents an empirical analysis of a Recurrent Neural
Network (RNN) model tailored for the detection of fake news, offering an in-depth examination of its performance on a testing
dataset. The RNN model demonstrated exceptional accuracy, achieving a 98.94% success rate in accurately distinguishing
between fake and real news articles, with a low loss value of 0.0372, indicating high precision in classification tasks. Key
performance metrics further elucidate the model's capabilities: a precision rate of approximately 98.73% underscores the
model's accuracy in identifying fake news. In comparison, a recall rate of about 99.07% highlights its proficiency in correctly
classifying a majority of fake news instances within the dataset. The synthesis of these results—accuracy, precision, and recall—
attests to the robustness of the RNN model as a highly reliable tool for discriminating between genuine and fabricated news
content. These findings not only reinforce the model's applicability in real-world scenarios, crucial for filtering misinformation
but also underscore its potential in maintaining informational integrity. This study paves the way for future research and
application in misinformation detection, signifying a substantial contribution to the field.
Keywords - Fake News Detection, Recurrent Neural Network (RNN), Machine Learning, Natural Language Processing (NLP),
Adaptive Algorithms, Real-time Analysis.