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

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.

Predicting internet addiction with the dark triad: Beyond the five-factor model.

“…Still, the forced entry of the five-factor model revealed significant outcomes. The significance of Conscientiousness and Neuroticism is in line with previous findings (see Di et al, 2019), hence the theoretical importance. Previously, the combination of high Neuroticism and low Conscientiousness led to instability and impul-This document is copyrighted by the American Psychological Association or one of its allied publishers.…”

“…Apart from the improved predictive utility, relating the Dark Triad with Internet addiction is vital to stimulate theoretical progression. Studies have consistently attributed the dark traits with impulsiveness and poor self-control (see Crysel et al, 2013; Jonason & Tost, 2010; Jones & Paulhus, 2011; Malesza & Ostaszewski, 2016), which is also a trait that enhances susceptibility to Internet addiction (Cao et al, 2007; Choi et al, 2014; Di et al, 2019; Shi & Du, 2019). As the Dark Triad traits (Crysel et al, 2013) and Internet addiction (Cao et al, 2007) are related to high impulsiveness and lack of self-regulation, it is theoretically plausible that these three erratic traits are instrumental to the development of technological addiction (Montag et al, 2011; Sariyska et al, 2014).…”

(Ne)zavisnost od igranja video-igara iz perspektive učenika nižih razreda osnovne škole

“…Mlade osobe (tinejdžeri) koji su zavisni od interneta, osim što provode znatan broj sati na internetu, konstantno i razgovaraju o utiscima nakon toga; odbijaju da učestvuju u društvenim aktivnostima kako bi proveli više vremena 'onlajn' 9 ; događaju im se izlivi besa, anksioznost, ili frustracije ako im se zabrani korišćenja interneta; uvek nanovo koriste internet bez obzira na probleme koje to izaziva u školi ili društvenim odnosima (Zorbaz, Ulas, Kizildag, 2015: 489-497). Takođe, postoje fizički i zdravstveni indikatori koji se javljaju: problemi sa zdravljem kao što su glavobolje i bolovi u leđima; problemi sa spavanjem; problemi sa vidom i suve oči; gubitak kilaže i izbegavanje obroka zbog korišćenja interneta; loša higijena (Di et al, 2019). Sve navedene simptome/indikatore potencijalne zavisnosti od interneta i ,,zarobljavanja u virtuelnom svetu", pogotovo u virtuelnim društvenim odnosnima, treba na vreme prepoznati i dalje, prema stepenu zavisnosti, delovati/lečiti.…”

“…Videti i:Rumpf, Batra, Mann (2014: 364-366), kao i TeWildt (2011: 80-82).4 Zavisnost se definiše kao želja koja se ne može sprečiti i koja se javlja kao rezultat konstantnog uzimanja određene supstance u sve većim dozama bez namere da se iskorene simptomi organske bolesti(Ziyalar, 1999, prema Zorbaz, Ulas, & Kizildag, 2015 Pojmovi zavisnost i zavisno ponašanje poseduju zajedničku karakteristiku, a to je "nemogućnost da se kontroliše radnja koja se ponavlja uprkos izazivanju loših rezultata"(Henderson, 2001).Primera radi, zdravstvena služba u Kini smatra šest sati neprekidnog korišćenja računara za granicu posle koje neko može da bude klasifikovan kao 'zavisnik' i biti upućen na lečenje. Vlada u Seulu je prihvatila da je zavisnost od interneta ozbiljan zdravstveni problem u zemlji -otvorila je savetovališta i pokrenula programe prevencije, ali rezultati su ograničeni, jer se kultura interneta nezadrživo širi pre svega među mladima(Di et al, 2019; Yeonsoo et al, 2010: 51-57). Itd.…”

Impulsivity-Compulsivity Axis: Evidence of Its Clinical Validity to Individually Classify Subjects on the Use/Abuse of Information and Communication Technologies

“…The use of Machine Learning techniques in the study of aspects related to addictive behaviors (with or without substances) has progressively increased in the last years, thus yielding a valuable work that will be useful in future clinical applications (Mak et al, 2019 ). The use of such techniques has recently proliferated to study concrete aspects on the use/abuse of ICT (Di et al, 2019 ; Kamaruddin et al, 2019 ; Xu et al, 2019 ; Gross et al, 2020 ).…”