TY - JOUR
T1 - Identification and prediction of Phubbing behavior
T2 - A data-driven approach
AU - Rahman, Md Anisur
AU - Duradoni, Mirko
AU - Guazzini, Andrea
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
PY - 2022/3
Y1 - 2022/3
N2 - Research on Phubbing has received a lot of attention in recent years from the research community. However, the studies conducted are mainly based on linear statistics, which is a very conservative method for data analysis. To overcome this limitation, we adopted a data mining and machine learning-based approach to identify the patterns related to Phubbing behavior. We developed several models on online survey data that we collected for our analysis purposes. The results highlighted that addiction measures fail to predict Phubbing fully. Indeed, Phubbing appeared to be linked in a nonlinear way to both Information and Communication Technology (ICT) measures that do not imply a dysfunctional use of technology and social anxiety. Moreover, the machine learning approach appeared more suitable than traditional linear statistics methods to predict Phubbing, as highlighted by a much higher explained variance. Phubbing is not solely attributable to addiction dynamics. Phubbing is indicated by a series of predictors that cannot be reduced to addiction (e.g., age, social anxiety, ICT services owned). Modeling procedures able to account for nonlinearity are also required to accurately assessing users’ Phubbing levels. The patterns produced by our modeling procedure may help scholars in accounting for phubbing definition, detection, and prediction more accurately.
AB - Research on Phubbing has received a lot of attention in recent years from the research community. However, the studies conducted are mainly based on linear statistics, which is a very conservative method for data analysis. To overcome this limitation, we adopted a data mining and machine learning-based approach to identify the patterns related to Phubbing behavior. We developed several models on online survey data that we collected for our analysis purposes. The results highlighted that addiction measures fail to predict Phubbing fully. Indeed, Phubbing appeared to be linked in a nonlinear way to both Information and Communication Technology (ICT) measures that do not imply a dysfunctional use of technology and social anxiety. Moreover, the machine learning approach appeared more suitable than traditional linear statistics methods to predict Phubbing, as highlighted by a much higher explained variance. Phubbing is not solely attributable to addiction dynamics. Phubbing is indicated by a series of predictors that cannot be reduced to addiction (e.g., age, social anxiety, ICT services owned). Modeling procedures able to account for nonlinearity are also required to accurately assessing users’ Phubbing levels. The patterns produced by our modeling procedure may help scholars in accounting for phubbing definition, detection, and prediction more accurately.
KW - Phubbing
KW - decision tree
KW - classification
KW - accuracy
KW - Accuracy
KW - Addiction
KW - Classification
KW - Decision tree
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U2 - 10.1007/s00521-021-06649-5
DO - 10.1007/s00521-021-06649-5
M3 - Article
SN - 0941-0643
VL - 34
SP - 3885
EP - 3894
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 5
ER -