Detection of network false information based on artificial intelligence models

Abstract

There is often some false information in social platforms to mislead public opinion. Due to the rapid development of the Internet, the spread of false information on the Internet has become easier, which has brought many losses to people's economy and life. In this paper, the relevant research on false information based on bidirectional convolutional networks is analyzed, and the method is divided into four stages: data preprocessing, model architecture, training process and prediction process. Then, the relevant research on rumor detection by propagation tree kernel model are analyzed. Finally, this paper delineates a comprehensive framework that amalgamates enhanced Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybridized Black Widow Optimization (BWO) with Moth Optimization Algorithm (MOA) (referred to as HM-BWO) for the accurate detection of network-based false information. This paper analyzes and discusses the challenges of poor universality and insufficient detection speed encountered by the current rumor detection research and puts forward the idea of introducing migration model to solve the problem of poor universality and Spark to solve the problem of insufficient detection speed. This article provides a good overview of the field of online disinformation.