The cross-cultural differences of network user behavior of new media technology platform using deep learning

Fang Jin and Yijun Liu ()
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Fang Jin: Zhejiang Yuexiu University
Yijun Liu: Zhejiang Yuexiu University

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 3, No 12, 1090 pages

Abstract: Abstract This exploration aims to explore how to use the new media technology of deep learning to analyze the cross-cultural differences between network users. First, the combination of edge computing and deep neural network model is adopted to complete data acquisition. Then, the network user behavior of the new media technology platform is analyzed. Finally, network user behavior and cross-cultural differences are studied. The survey data show that the interaction between new media technology and cross-cultural communication is very complex, which depends on the user behavior of the network society. Different behaviors of users produce different cultural exchange contents. Besides, most users of the new media technology platform will form independent individuals because they accept the past culture and ideas. The differences among individuals are closely related to their situation. There are also some differences in hobbies, habits and behaviors. People of different nationalities, skin colors, races and beliefs communicate on the network platform, because their communication crosses the obstacles of distance, time and language to a certain extent, and realizes cross-cultural interaction. Hence, for a country with a long cultural heritage and splendid historical civilization, the network society can significantly reflect the cultural differences between countries. The inclusiveness and openness of national culture provide great advantages for people to create new culture. This exploration provides a reference for relevant research and has a certain reference significance.

Keywords: Deep neural network; Cross-cultural differences; Edge computing; Network user behavior; New media technology platform (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01430-2

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