Using Behavior Data to Predict the Internet Addiction of College Students

• Published in: Web Information Systems and Applications Vol. 11817; pp. 151 - 162
• Main Authors: Peng, Wei, Zhang, Xinlei, Li, Xin
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Internet addiction; Neural network; Student behavior data
• ISBN: 9783030309510; 3030309517
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-30952-7_17

Internet addiction refers to excessive internet use that interferes with daily life. Due to its negative impact on college students’ study and life, discovering students’ internet addiction tendencies and making correct guidance for them timely are necessary. However, at present, the research methods used on analyzing students’ internet addiction are mainly questionnaire and statistical analysis which relays on the domain experts heavily. Fortunately, with the development of the smart campus, students’ behavior data such as consumption and trajectory information in the campus are stored. With this information, we can analyze students’ internet addiction level quantitatively. In this paper, we provide an approach to estimate college students’ internet addiction level using their behavior data in the campus. In detail, we consider students’ addiction towards internet is a hidden variable which affects students’ daily time online together with other behavior. By predicting students’ daily time online, we will find students’ internet addiction levels. Along this line, we develop a linear internet addiction (LIA) model and a neural network internet addiction (NIA) model to calculate students’ internet addiction level respectively. And several experiments are conducted on a real-world dataset. The experimental results show the effectiveness of our method, and it’s also consistent with some psychological findings.