Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/19881
Title: | Internet addiction disorder detection of Chinese college students using several personality questionnaire data and support vector machine |
Authors: | Di, Z Gong, X Shi, J Ahmed, HOA Nandi, AK |
Keywords: | Internet addiction (IA);IA detection;Personality questionnaire;Feature selection;Support vector machine |
Issue Date: | 2019 |
Publisher: | Elsevier |
Citation: | Addictive Behaviors Reports, 2019, 10 |
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. |
URI: | http://bura.brunel.ac.uk/handle/2438/19881 |
DOI: | http://dx.doi.org/10.1016/j.abrep.2019.100200 |
ISSN: | http://dx.doi.org/10.1016/j.abrep.2019.100200 2352-8532 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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