@article{Alibasa:2023:10.1080/10447318.2022.2073321,
author = {Alibasa, MJ and Calvo, RA and Yacef, K},
doi = {10.1080/10447318.2022.2073321},
journal = {International Journal of Human-Computer Interaction},
pages = {2061--2075},
title = {Predicting mood from digital footprints using frequent sequential context patterns features},
url = {http://dx.doi.org/10.1080/10447318.2022.2073321},
volume = {39},
year = {2023}
}

Download

TY  - JOUR
AB  - Understanding the relationship between technology and wellbeing is important in order to raise awareness and to improve interaction designs with digital technologies. Most studies used the time spent and frequency information of digital technology usage, very few explored the sequences and the patterns of how the activity occurs. We introduce the concept of “digital context,” a representation of activity data occurring in a short time-window. Using data from our study, we determined whether: (1) there are digital context patterns that are more frequent in a particular mood compared to other moods; and (2) in the case such patterns exist, whether they can be used to improve the performance of mood prediction models. Our results showed that a mood prediction model that include digital context features yielded an accuracy of 77.8%, which is an improvement compared with the models proposed in past studies.
AU  - Alibasa,MJ
AU  - Calvo,RA
AU  - Yacef,K
DO  - 10.1080/10447318.2022.2073321
EP  - 2075
PY  - 2023///
SN  - 1044-7318
SP  - 2061
TI  - Predicting mood from digital footprints using frequent sequential context patterns features
T2  - International Journal of Human-Computer Interaction
UR  - http://dx.doi.org/10.1080/10447318.2022.2073321
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000811096300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.tandfonline.com/doi/full/10.1080/10447318.2022.2073321
UR  - http://hdl.handle.net/10044/1/98211
VL  - 39
ER  - 

Download