Analysis of College Students' Online Social Media Behavior and Its Impact on Mental Health

被引：0
|
作者：
Chen, Shaojie [1 ]
Li, Si [2 ]
Chen, Yuzhe [2 ]
Lin, Jianjun [2 ]
机构：
[1] School of Environmental Science and Engineering, Xiamen University of Technology, Fujian, Xiamen,361024, China
[2] School of Mathematics and Statistics, Xiamen University of Technology, Fujian, Xiamen,361024, China
来源：
Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期
关键词：
Cost functions - Entropy - Feature extraction - Long short-term memory - Social networking (online) - Students;
D O I：
10.2478/amns-2024-1160
中图分类号：
学科分类号：
摘要：
To explore the correlation between college students' social media behaviors and their mental health in the context of the network era in order to provide some empirical evidence for college students' mental health education in the new period and new situation. In this paper, we first introduce the attention mechanism to optimize the LSTM network model to extract the features of college students' online social media behaviors and combine it with the weighted cross-entropy cost function to improve the speed of the model when extracting behavioral features. Then, the fitness function of the genetic algorithm is used to analyze the distance between the feature samples, and the optimal features are selected through the selection strategy and cross-variance operation to obtain the college students' online social media behavior. The analysis of the impact of social media behavior on mental health found that there is a significant difference in the level of misplaced fear and self-differentiation of students with different average use times of social media (P=0.024 © 2024 Shaojie Chen, et al., published by Sciendo.
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