Identification and Modeling of College Students’ Loneliness from the Perspective of Big Data a Machine Learning-Based Approach
- 10.2991/978-94-6463-034-3_43How to use a DOI?
- Machine Learning; Loneliness; Algorithms
With the advent of the era of big data, the information society inevitably intersects and integrates with everyone's life. Compared with the traditional self-statement scale for psychological measurement, big data network information has advantages such as excellent ecological validity. In this paper, we use the consumption data and access control data of college students in a university as the data source and the employment situation as the target variable to identify and predict the loneliness of college students. In the context of the current COVID-19 epidemic, which is generally in quarantine, this paper provides a realistic basis for this study. By extracting the features from the data, we address the limitations of the machine learning modeling approach for the autonomous identification and prediction of loneliness symptoms and propose further development prospects. This paper provides a practical basis for this research. By extracting features from the data information, we propose the limitations of the machine learning modeling approach for the autonomous identification and prediction of symptoms of loneliness, and propose the future development of this approach.
- © 2023 The Author(s)
- Open Access
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Cite this article
TY - CONF AU - Yuexiang Liu AU - Huajie Sui AU - Luming Feng PY - 2022 DA - 2022/12/23 TI - Identification and Modeling of College Students’ Loneliness from the Perspective of Big Data a Machine Learning-Based Approach BT - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) PB - Atlantis Press SP - 418 EP - 425 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-034-3_43 DO - 10.2991/978-94-6463-034-3_43 ID - Liu2022 ER -