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DC Field | Value | Language |
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dc.contributor.author | Di, Z | - |
dc.contributor.author | Gong, X | - |
dc.contributor.author | Shi, J | - |
dc.contributor.author | Ahmed, HOA | - |
dc.contributor.author | Nandi, AK | - |
dc.date.accessioned | 2019-12-18T16:21:24Z | - |
dc.date.available | 2019-12-01 | - |
dc.date.available | 2019-12-18T16:21:24Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Addictive Behaviors Reports, 2019, 10 | en_US |
dc.identifier.issn | http://dx.doi.org/10.1016/j.abrep.2019.100200 | - |
dc.identifier.issn | 2352-8532 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/19881 | - |
dc.description.abstract | © 2019 The Authors With the unprecedented development of the Internet, it also brings the challenge of Internet Addiction (IA), which is hard to diagnose and cure according to the state-of-art research. In this study, we explored the feasibility of machine learning methods to detect IA. We acquired a dataset consisting of 2397 Chinese college students from the University (Age: 19.17 ± 0.70, Male: 64.17%) who completed Brief Self Control Scale (BSCS), the 11th version of Barratt Impulsiveness Scale (BIS-11), Chinese Big Five Personality Inventory (CBF-PI) and Chen Internet Addiction Scale (CIAS), where CBF-PI includes five sub-features (Openness, Extraversion, Conscientiousness, Agreeableness, and Neuroticism) and BSCS includes three sub-features (Attention, Motor and Non-planning). We applied Student's t-test on the dataset for feature selection and Support Vector Machines (SVMs) including C-SVM and ν-SVM with grid search for the classification and parameters optimization. This work illustrates that SVM is a reliable method for the assessment of IA and questionnaire data analysis. The best detection performance of IA is 96.32% which was obtained by C-SVM in the 6-feature dataset without normalization. Finally, the BIS-11, BSCS, Motor, Neuroticism, Non-planning, and Conscientiousness are shown to be promising features for the detection of IA. | en_US |
dc.description.sponsorship | Fundamental Research Funds for the Central Universities of Tongji University (22120170043, 22120180542) and the Natural Science Foundation of Shanghai grant number 16JC1401300 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | Internet addiction (IA) | en_US |
dc.subject | IA detection | en_US |
dc.subject | Personality questionnaire | en_US |
dc.subject | Feature selection | en_US |
dc.subject | Support vector machine | en_US |
dc.title | Internet addiction disorder detection of Chinese college students using several personality questionnaire data and support vector machine | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/j.abrep.2019.100200 | - |
dc.relation.isPartOf | Addictive Behaviors Reports | - |
pubs.publication-status | Published | - |
pubs.volume | 10 | - |
dc.identifier.eissn | 2352-8532 | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | 1.06 MB | Adobe PDF | View/Open |
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