Differences in smartphone usage: validating, evaluating, and predicting mobile user intimacy

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Differences in smartphone usage : validating, evaluating, and predicting mobile user intimacy. / Gustarini, Mattia; Scipioni, Marcello Paolo; Fanourakis, Marios; Wac, Katarzyna.

In: Pervasive and Mobile Computing, Vol. 33, 2016, p. 50-72.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Gustarini, M, Scipioni, MP, Fanourakis, M & Wac, K 2016, 'Differences in smartphone usage: validating, evaluating, and predicting mobile user intimacy', Pervasive and Mobile Computing, vol. 33, pp. 50-72. https://doi.org/10.1016/j.pmcj.2016.06.003

APA

Gustarini, M., Scipioni, M. P., Fanourakis, M., & Wac, K. (2016). Differences in smartphone usage: validating, evaluating, and predicting mobile user intimacy. Pervasive and Mobile Computing, 33, 50-72. https://doi.org/10.1016/j.pmcj.2016.06.003

Vancouver

Gustarini M, Scipioni MP, Fanourakis M, Wac K. Differences in smartphone usage: validating, evaluating, and predicting mobile user intimacy. Pervasive and Mobile Computing. 2016;33:50-72. https://doi.org/10.1016/j.pmcj.2016.06.003

Author

Gustarini, Mattia ; Scipioni, Marcello Paolo ; Fanourakis, Marios ; Wac, Katarzyna. / Differences in smartphone usage : validating, evaluating, and predicting mobile user intimacy. In: Pervasive and Mobile Computing. 2016 ; Vol. 33. pp. 50-72.

Bibtex

@article{1d0859e3df984700a9558ed0a482b543,
title = "Differences in smartphone usage: validating, evaluating, and predicting mobile user intimacy",
abstract = "We analyze the users{\textquoteright} intimacy to investigate the differences in smartphone usage, considering the user{\textquoteright}s location and number and kind of people physically around the user. With a first user study we (1) validate the intimacy concept, (2) evaluate its correlation to smartphone usage features and (3) we computationally model it. Shorter, more frequent, and less engaging interactions take place when intimacy is lower, while longer, less frequent, and engaging interactions when intimacy is higher. With a second user study, we investigate the intimacy predictability in practice. Location-time features are predictive for the intimacy, and other smartphone-based features can improve the intimacy prediction accuracy.",
author = "Mattia Gustarini and Scipioni, {Marcello Paolo} and Marios Fanourakis and Katarzyna Wac",
year = "2016",
doi = "10.1016/j.pmcj.2016.06.003",
language = "English",
volume = "33",
pages = "50--72",
journal = "Pervasive and Mobile Computing",
issn = "1574-1192",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Differences in smartphone usage

T2 - validating, evaluating, and predicting mobile user intimacy

AU - Gustarini, Mattia

AU - Scipioni, Marcello Paolo

AU - Fanourakis, Marios

AU - Wac, Katarzyna

PY - 2016

Y1 - 2016

N2 - We analyze the users’ intimacy to investigate the differences in smartphone usage, considering the user’s location and number and kind of people physically around the user. With a first user study we (1) validate the intimacy concept, (2) evaluate its correlation to smartphone usage features and (3) we computationally model it. Shorter, more frequent, and less engaging interactions take place when intimacy is lower, while longer, less frequent, and engaging interactions when intimacy is higher. With a second user study, we investigate the intimacy predictability in practice. Location-time features are predictive for the intimacy, and other smartphone-based features can improve the intimacy prediction accuracy.

AB - We analyze the users’ intimacy to investigate the differences in smartphone usage, considering the user’s location and number and kind of people physically around the user. With a first user study we (1) validate the intimacy concept, (2) evaluate its correlation to smartphone usage features and (3) we computationally model it. Shorter, more frequent, and less engaging interactions take place when intimacy is lower, while longer, less frequent, and engaging interactions when intimacy is higher. With a second user study, we investigate the intimacy predictability in practice. Location-time features are predictive for the intimacy, and other smartphone-based features can improve the intimacy prediction accuracy.

U2 - 10.1016/j.pmcj.2016.06.003

DO - 10.1016/j.pmcj.2016.06.003

M3 - Journal article

VL - 33

SP - 50

EP - 72

JO - Pervasive and Mobile Computing

JF - Pervasive and Mobile Computing

SN - 1574-1192

ER -

ID: 166605857