Di Zonglin, Gong Xiaoliang, Shi Jingyu, Ahmed Hosameldin O A, Nandi Asoke K
School of Electronic and Information Engineering, Tongji University, Shanghai, China.
East Hospital, Tongji University School of Medicine, Shanghai 200120, China.
Addict Behav Rep. 2019 Jul 11;10:100200. doi: 10.1016/j.abrep.2019.100200. eCollection 2019 Dec.
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.
随着互联网的空前发展，它也带来了网络成瘾（IA）的挑战，根据现有研究，这种成瘾难以诊断和治愈。在本研究中，我们探讨了机器学习方法检测网络成瘾的可行性。我们获取了一个数据集，该数据集由来自某大学的2397名中国大学生组成（年龄：19.17 ± 0.70，男性：64.17%），他们完成了简短自我控制量表（BSCS）、第11版巴拉特冲动性量表（BIS - 11）、中国大五人格量表（CBF - PI）和陈网络成瘾量表（CIAS），其中CBF - PI包括五个子特征（开放性、外向性、尽责性、宜人性和神经质），BSCS包括三个子特征（注意力、行为和非计划性）。我们对数据集应用学生t检验进行特征选择，并使用支持向量机（SVM）（包括C - SVM和ν - SVM）并通过网格搜索进行分类和参数优化。这项工作表明，支持向量机是评估网络成瘾和问卷数据分析的可靠方法。在未进行归一化处理的6特征数据集中，C - SVM获得的网络成瘾最佳检测性能为96.32%。最后，结果表明BIS - 11、BSCS、行为、神经质、非计划性和尽责性是检测网络成瘾很有前景的特征。
