Social media platform-oriented topic mining and information security analysis by big data and deep convolutional neural network

Changlin Wang

Technological Forecasting and Social Change, 2024, vol. 199, issue C

Abstract: The aim of this work is to conduct topic mining and data analysis of social network security using social network-based big data. The deep convolution neural network (DCNN) is used to analyze social network security issues. Traditional neural network models cannot handle long sequence data when extracting information on Weibo security topics. Thus, the long short-term memory (LSTM) structure in the memory intelligence algorithm extracts Weibo topic information. Specifically, the social network security topics are mined through Big Data, and CNN searches Weibo security topics. CNN can learn the grammar and semantic information of Weibo topics to obtain in-depth data features. Afterward, the performance of the improved DCNN model is compared with the AlexNet, Convolutional Neural Network (CNN), and Deep Neural Network (DNN) by considering the model's accuracy, recall, and F1 value, respectively. The experimental results show that after 120 iterations, the recognition accuracy of the improved DCNN model peaks at 96.17 %, at least 5.4 % superior to the other three models. Additionally, the intrusion detection model's accuracy, recall, and F1 value are 88.57 %, 75.22 %, and 72.05 %, respectively. In the worst case, the constructed model's accuracy, recall, and F1 value are 3.1 % higher than those of the other methods. The training and testing time consumption of the improved DCNN security detection model stabilized at 65.86 s and 27.90 s, much shorter than similar literature approaches. The experimental conclusion is that the improved DCNN under deep learning has the characteristic of lower delay, and the model shows good network data security transmission.

Keywords: Social network; Big data; Deep convolution neural network; Network security; Topic mining (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:199:y:2024:i:c:s0040162523007552

DOI: 10.1016/j.techfore.2023.123070

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