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

Clicks: 498
ID: 42807
2019
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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 -test on the dataset for feature selection and Support Vector Machines (SVMs) including C-SVM and -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.
Reference Key
di2019internetaddictive Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Di, Zonglin;Gong, Xiaoliang;Shi, Jingyu;Ahmed, Hosameldin O A;Nandi, Asoke K;
Journal addictive behaviors reports
Year 2019
DOI
10.1016/j.abrep.2019.100200
URL
Keywords
support vector machine feature selection ia detection internet addiction (ia) personality questionnaire

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