Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023)

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Machine Learning Assessment of Factors Associated with Social Media Addiction Among Chinese College Students

Authors
Shuang Luo1, *, Mengke Zhu1
1School of International Education, Xiamen University of Technology, Xiamen, China
*Corresponding author. Email: luoshuang@xmut.edu.cn
Corresponding Author
Shuang Luo
Available Online 30 June 2023.
DOI
10.2991/978-94-6463-172-2_179How to use a DOI?
Keywords
social media addiction; college students; machine learning; random forest; Pearson correlation; predictive factors
Abstract

Research has showed that the excessive use of social media has an impact on the user’s functioning. Young students are among the most vulnerable to the impacts of social media addiction. This study aims to determine the status of social media addiction amongst Chinese college students and access the factors associated with such an addiction. An online survey was conducted among Chinese college students and the collected data were analyzed in IBM SPSS Statistics 29 descriptively. A machine learning algorithm, the random forest, was deployed to identify and Pearson correlation analysis was implemented to confirm important variables related to social media addiction. Results show that female respondents had higher scores than males and students in their third or fourth year of study had higher score than those in the first or second year of study. Participants majoring in Arts and Humanities also had higher addiction levels than those majoring in STEM disciplines. Fear of missing out and stress of online neglect, as well as hours spent on social media daily, had a clear correlation with social media addiction.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
30 June 2023
ISBN
10.2991/978-94-6463-172-2_179
ISSN
2589-4900
DOI
10.2991/978-94-6463-172-2_179How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shuang Luo
AU  - Mengke Zhu
PY  - 2023
DA  - 2023/06/30
TI  - Machine Learning Assessment of Factors Associated with Social Media Addiction Among Chinese College Students
BT  - Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023)
PB  - Atlantis Press
SP  - 1612
EP  - 1620
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-172-2_179
DO  - 10.2991/978-94-6463-172-2_179
ID  - Luo2023
ER  -