Internet addiction disorder detection of Chinese college students using several personality questionnaire data and support vector machine. (December 2019)

• Record Type: Journal Article
• Authors: Di, Zonglin; Gong, Xiaoliang; Shi, Jingyu; Ahmed, Hosameldin O.A.; Nandi, Asoke K.
• Is Part Of: Addictive behaviors reports. Volume 10(2019)
• Publication Date: 2019-12
• Subjects: Internet addiction (IA) -- IA detection -- Personality questionnaire -- Feature selection -- Support vector machine; Compulsive behavior -- Periodicals; 616.8584
• DOI: 10.1016/j.abrep.2019.100200
• Languages: English
• ISSNs: 2352-8532

Abstract

Abstract: 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. Highlights: Combining grid search and SVM has improved the detection performance of Internet Addiction Disorder (IAD). 6 sub-scales of personality are found to be better features for the detection of IAD. The best detection accuracy is 96.32% from C-SVM with 6 selected features. Multiple feature investigation for IAD detection.