Fake News Detection Using Deep Neuro-Fuzzy Network

In this study, we introduce an innovative network architecture that synergizes fuzzy neural networks with positional self-attention mechanisms to enhance fake news detection. This approach effectively addresses emerging challenges posed by new fake news technologies, aiming to bolster detection accuracy, protect public interests, and support credible media development. By integrating diverse information sources, including textual content and semantic nuances, our model excels in processing ambiguous data and discerning subtle variances in news authenticity. The utilization of fuzzy neural networks allows for adept handling of uncertain information, while positional self-attention coding proficiently identifies the significance of different textual elements, offering a nuanced analysis of news veracity. Our extensive experiments on two datasets reveal a substantial improvement in detection accuracy, with the model achieving an accuracy increase of over 15% compared to traditional methods. This work not only demonstrates a methodological advancement in tackling fake news but also contributes significantly to upholding social integrity and public trust.