TY - JOUR
T1 - Internet addiction disorder detection of Chinese college students using several personality questionnaire data and support vector machine
AU - Di, Z.
AU - Gong, X.
AU - Shi, J.
AU - Ahmed, H.O.A.
AU - Nandi, A.K.
PY - 2019/8/30
Y1 - 2019/8/30
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85071500737&partnerID=MN8TOARS
UR - https://www.clinicalkey.com/#!/content/playContent/1-s2.0-S2352853218301512?returnurl=https:%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2352853218301512%3Fshowall%3Dtrue&referrer=
U2 - 10.1016/j.abrep.2019.100200
DO - 10.1016/j.abrep.2019.100200
M3 - Article
SN - 2352-8532
VL - 10
JO - Addictive Behaviors Reports
JF - Addictive Behaviors Reports
M1 - 100200
ER -