Predicting Mood from Digital Footprints Using Frequent Sequential Context Patterns Features

Muhammad Johan Alibasa

doi:10.1080/10447318.2022.2073321

Predicting Mood from Digital Footprints Using Frequent Sequential Context Patterns Features

2022-06-14

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Muhammad Johan Alibasa

DOI：10.1080/10447318.2022.2073321

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Abstract

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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.

Keywords:

STRESS DETECTION

COMMUNICATION

RECOGNITION

EMOTION

FUSION

Predicting Mood from Digital Footprints Using Frequent Sequential Context Patterns Features

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