Internet addiction disorder detection of Chinese college students using several personality questionnaire data and support vector machine Article Swipe
Zonglin Di
,
Xiaoliang Gong
,
Jingyu Shi
,
Hosameldin Ahmed
,
Asoke K. Nandi
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1016/j.abrep.2019.100200
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1016/j.abrep.2019.100200
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.
Related Topics
Concepts
Support vector machine
Conscientiousness
Artificial intelligence
Machine learning
Neuroticism
Openness to experience
Agreeableness
Extraversion and introversion
Computer science
Psychology
Big Five personality traits
Personality
Applied psychology
Social psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.abrep.2019.100200
- OA Status
- gold
- Cited By
- 42
- References
- 105
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2959528333
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2959528333Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.abrep.2019.100200Digital Object Identifier
- Title
-
Internet addiction disorder detection of Chinese college students using several personality questionnaire data and support vector machineWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-07-11Full publication date if available
- Authors
-
Zonglin Di, Xiaoliang Gong, Jingyu Shi, Hosameldin Ahmed, Asoke K. NandiList of authors in order
- Landing page
-
https://doi.org/10.1016/j.abrep.2019.100200Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.abrep.2019.100200Direct OA link when available
- Concepts
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Support vector machine, Conscientiousness, Artificial intelligence, Machine learning, Neuroticism, Openness to experience, Agreeableness, Extraversion and introversion, Computer science, Psychology, Big Five personality traits, Personality, Applied psychology, Social psychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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42Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 7, 2023: 6, 2022: 7, 2021: 11Per-year citation counts (last 5 years)
- References (count)
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105Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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