An Evolutionary Fake News Detection Based on Tropical Convolutional Neural Networks (TCNNs) Approach

Abstract

In general, the characteristics of false news are difficult to distinguish from those of legitimate news. Even if it is wrong, people can make money by spreading false information. A long time ago, there were fake news stories, including the one about "Bat-men on the moon" in 1835. A mechanism for fact-checking statements must be put in place, particularly those that garner thousands of views and likes before being refuted and proven false by reputable sources. Many machine learning algorithms have been used to precisely categorize and identify fake news. In this experiment, an ML classifier was employed to distinguish between fake and real news. In this study, we present a Tropical Convolutional Neural Networks (TCNNs) model-based false news identification system. Convolutional neural networks (CNNs), Gradient Boost, long short-term memory (LSTMs), Random Forest, Decision Tree (DT), Ada Boost, and attention mechanisms are just a few of the cutting-edge techniques that are compared in our study. Furthermore, because tropical convolution operators are fundamentally nonlinear operators, we anticipate that TCNNs will be better at nonlinear fitting than traditional CNN. Our analysis leads us to the conclusion that the Tropical Convolutional Neural Networks (TCNNs) model with attention mechanism has the maximum accuracy of 98.93%. The findings demonstrate that TCNN can outperform regular convolutional neural network (CNN) layers in terms of expressive capability.