Intelligent Behavior Data Analysis for Internet Addiction
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 is necessary. However, at present, the research methods...
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2019-01-01
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Online Access: | http://dx.doi.org/10.1155/2019/2753152 |
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doaj-673e301638c74c1fa93a384d13fc6ad32021-07-02T05:17:43ZengHindawi LimitedScientific Programming1058-92441875-919X2019-01-01201910.1155/2019/27531522753152Intelligent Behavior Data Analysis for Internet AddictionWei Peng0Xinlei Zhang1Xin Li2Information Technology Support, East China Normal University, Shanghai 200062, ChinaShanghai Key Laboratory of Trustworthy Computing, MOE International Joint Lab of Trustworthy Software, East China Normal University, Shanghai 200062, ChinaShanghai Key Laboratory of Trustworthy Computing, MOE International Joint Lab of Trustworthy Software, East China Normal University, Shanghai 200062, ChinaInternet 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 is necessary. However, at present, the research methods used in analyzing students’ internet addiction are mainly questionnaires and statistical analysis, which relies 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 levels quantitatively. In this paper, we provide an approach to estimate college students’ internet addiction levels using their behavior data in the campus. In detail, we consider students’ addiction towards the 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, a neural network internet addiction (NIA) model, and a clustering-based internet addiction (CIA) model to calculate students’ internet addiction levels, respectively. These three models take the regularity of students’ behavior and the similarity among students’ behavior into consideration. Finally, extensive experiments are conducted on a real-world dataset. The experimental results show the effectiveness of our method, and it is also consistent with some psychological findings.http://dx.doi.org/10.1155/2019/2753152 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Peng Xinlei Zhang Xin Li |
spellingShingle |
Wei Peng Xinlei Zhang Xin Li Intelligent Behavior Data Analysis for Internet Addiction Scientific Programming |
author_facet |
Wei Peng Xinlei Zhang Xin Li |
author_sort |
Wei Peng |
title |
Intelligent Behavior Data Analysis for Internet Addiction |
title_short |
Intelligent Behavior Data Analysis for Internet Addiction |
title_full |
Intelligent Behavior Data Analysis for Internet Addiction |
title_fullStr |
Intelligent Behavior Data Analysis for Internet Addiction |
title_full_unstemmed |
Intelligent Behavior Data Analysis for Internet Addiction |
title_sort |
intelligent behavior data analysis for internet addiction |
publisher |
Hindawi Limited |
series |
Scientific Programming |
issn |
1058-9244 1875-919X |
publishDate |
2019-01-01 |
description |
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 is necessary. However, at present, the research methods used in analyzing students’ internet addiction are mainly questionnaires and statistical analysis, which relies 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 levels quantitatively. In this paper, we provide an approach to estimate college students’ internet addiction levels using their behavior data in the campus. In detail, we consider students’ addiction towards the 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, a neural network internet addiction (NIA) model, and a clustering-based internet addiction (CIA) model to calculate students’ internet addiction levels, respectively. These three models take the regularity of students’ behavior and the similarity among students’ behavior into consideration. Finally, extensive experiments are conducted on a real-world dataset. The experimental results show the effectiveness of our method, and it is also consistent with some psychological findings. |
url |
http://dx.doi.org/10.1155/2019/2753152 |
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