Impact of mood changes on application selection

Visuri, Aku; Sarsenbayeva, Zhanna; Gonçalves, Jorge; Karapanos, Evangelos; Jones, Simon L. Peyton

doi:10.1145/2968219.2968317

Cited by 11 publications

(6 citation statements)

References 10 publications

(6 reference statements)

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“…From four previously published studies [10,14,35,37], we aggregate a dataset of 347,576 smartphone screen entries, from 445 unique users between February 2014 and May 2016, which we then transform into 120277 unique usage sessions. Each session has an associated session length (M = 229.34 seconds), and the daily session count (M = 60.08) for that user on that particular day.…”

Section: Comparison To Smartphone Datasetmentioning

confidence: 99%

Quantifying Sources and Types of Smartwatch Usage Sessions

Visuri

Sarsenbayeva

Berkel

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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We seek to quantify smartwatch use, and establish differences and similarities to smartphone use. Our analysis considers use traces from 307 users that include over 2.8 million notifications and 800,000 screen usage events, and we compare our findings to previous work that quantifies smartphone use. The results show that smartwatches are used more briefly and more frequently throughout the day, with half the sessions lasting less than 5 seconds. Interaction with notifications is similar across both types of devices, both in terms of response times and preferred application types. We also analyse the differences between our smartwatch dataset and a dataset aggregated from four previously conducted smartphone studies. The similarities and differences between smartwatch and smartphone use suggest effect on usage that go beyond differences in form factor.

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Section: Comparison To Smartphone Datasetmentioning

confidence: 99%

Quantifying Sources and Types of Smartwatch Usage Sessions

Visuri

Sarsenbayeva

Berkel

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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“…Emotion-sensing has long been in the focus of Human-Computer Interaction (HCI) and ubicomp research -even giving rise to a new research field: Affective Computing [18]. Recent changes in consumer behavior and technological progress have created new opportunities for research and for the application of emotion detection technology in everyday life, e.g., emotion detection from smartphone usage behavior [19,20,24]. But, critical voices questioning the actual ability to detection emotion have become more prominent.…”

Section: Objectives and Impactmentioning

confidence: 99%

Making Sense of Emotion-Sensing: Workshop on Quantifying Human Emotions

Tag

Webber

Wadley

et al. 2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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The global pandemic and the uncertainty if and when life will return to normality have motivated a series of studies on human mental health. This research has elicited evidence for increasing numbers of anxiety, depression, and overall impaired mental well-being. But, the global COVID-19 pandemic has also created new opportunities for research into quantifying human emotions: remotely, contactless, in everyday life. The ubiquitous computing community has long been at the forefront of developing, testing, and building userfacing systems that aim at quantifying human emotion. However, rather than aiming at more accurate sensing algorithms, it is time to critically evaluate whether it is actually possible and in what ways it could be beneficial for technologies to be able to detect user emotions. In this workshop, we bring together experts from the fields of Ubiquitous Computing, Human-Computer Interaction, and Psychology to -long-overdue -merge their expertise and ask the fundamental questions: how do we make sense of emotion-sensing, can and should we quantify human emotions? CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Ubiquitous and mobile computing; • Applied computing → Psychology.

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“…There have also been several attempts to derive users' emotional state from application use data. In a study by Visuri et al [99], the authors map users' affective state to applications use. They found that when experiencing positive affect, the users tend to use Media, Games, Maps and Travel applications.…”

Section: Application Use Behaviourmentioning

confidence: 99%

Does Smartphone Use Drive our Emotions or vice versa? A Causal Analysis

Sarsenbayeva

Marini

Berkel

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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In this paper, we demonstrate the existence of a bidirectional causal relationship between smartphone application use and user emotions. In a two-week long in-the-wild study with 30 participants we captured 502,851 instances of smartphone application use in tandem with corresponding emotional data from facial expressions. Our analysis shows that while in most cases application use drives user emotions, multiple application categories exist for which the causal effect is in the opposite direction. Our findings shed light on the relationship between smartphone use and emotional states. We furthermore discuss the opportunities for research and practice that arise from our findings and their potential to support emotional well-being.

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Impact of mood changes on application selection

Cited by 11 publications

References 10 publications

Quantifying Sources and Types of Smartwatch Usage Sessions

Quantifying Sources and Types of Smartwatch Usage Sessions

Making Sense of Emotion-Sensing: Workshop on Quantifying Human Emotions

Does Smartphone Use Drive our Emotions or vice versa? A Causal Analysis

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