Hybrid Feature Extraction and Capsule Neural Network Model for Fake News Detection

Abstract

The introduction and widespread use of social media has altered how information is generated and disseminated, along with the expansion of the Internet. Through social media, information is now more quickly, cheaply, and easily available. Particularly harmful content includes misinformation propagated by social media users, such as false news. Users find it simple to post comments and false information on social networks. Realising the difference between authentic and false news is the biggest obstacle. The current study's aim of identifying bogus news involved the deployment of a capsule neural network. However, with time, this technique as a whole learns how to report user accuracy. This paper offers a three-step strategy for spotting bogus news on social networks as a solution to this issue. Pre-processing is executed initially to transform unstrsuctrured data into a structured form. The second part of the project brought the HFEM (Combined Feature Extraction Model), which also revealed new relationships between themes, authors, and articles as well as undiscovered features of false news. based on a collection of traits that were explicitly and implicitly collected from text. This study creates a capsule neural network model in the third stage to concurrently understand how creators, subjects, and articles are presented. This work uses four performance metrics in evaluations of the suggested classification algorithm using on existing public data sets.