Network Rumor Detection Method Using Deep Learning in Big Data Environment

Abstract

Aiming to achieve efficient and accurate network rumor detection, this paper proposes a rumor detection method based on deep learning network. First, this method uses API interface and web crawler to construct a large data set of information samples on the microblog platform. Then, this method processes and analyzes the large data set through multiple embedded layers to provide a complete and reliable analysis data set for the detection model. The rumor detection model is composed of bidirectional long- and short-term memory (Bi-LSTM) network and convolutional neural network (CNN), which can realize deep and efficient feature extraction for the analysis data set and ensure the optimal performance of rumor detection method. The simulation results show that the precision and accuracy of the proposed method are 0.9771 and 0.9726, respectively, which are better than the comparison algorithms. The proposed method has effective network rumor screening and identification performance.