Hi, I am Soowon Kang.
I am going to present this paper, "understanding emotion changes in mobile
experience sampling."
These days, many people have tried to record a wide range of information from
mobile and wearable devices.
The mobile applications pay attention to extract users’ health status, preferences,
behavior patterns, and even psychological states such as emotions.
In that regard, an in-situ assessment of people’s psycho-affective states is
essential for mobile and wearable sensing apps.
The experience sampling method; ESM has been widely employed to collect various
real-time behaviors and experiences in people's daily life.
This can help capture the target phenomena as naturally as possible.
However, since ESM uses self-report behaviors, we need to focus on the effects
of interruptions during ESM response tasks.
The previous interruptibility studies revealed that unexpected interruptions can
influence people's emotional states negatively.
So this presentation is going to introduce the effects of ESM interruptions and the
implications of using the ESM.
The figure showed a sequential description of the interruption in the context of ESM
response tasks.
In that regard, we will deeply understand what happens during the
ESM response task in three steps.
1) the amount of emotion changes during ESM response tasks.
2) the factors related to the emotion change.
3) real-life instances of the emotion change
We first consider how to measure the emotion change phenomenon.
Since our pilot study revealed that people can differentiate the emotions before and
after asking the ESM response tasks, we designed an ESM questionnaire to collect
the emotions before ESM tasks and emotion changes after ESM tasks.
The following 7-point Likert scaled questions were used in our study.
From Q1 to Q5, the participants answered their status before ESM tasks.
Q6 and Q7 asked their feelings during the ESM responses.
With these questionnaires, we conduct 7 days of data collection to get a proper
amount of analysis data.
We used participants' own smartphones to collect their ESM responses,
device usage data, and other sensor data.
Here are the details of our dataset.
After filtering out several invalid samples, we collected 2,227 ESM responses
from 78 participants.
For RQ1, we calculated per-person averaged responses
and this table summarized the results. About 40% of the responses
showed that there were emotion changes negatively or positively.
This amount of emotion changes cannot be overlooked when
collecting the emotion dataset.
For RQ2, we built a multi-level regression model with ESM labels,
demographic information, and the features extracted from MS band 2 and smartphone usage data.
As a result, we revealed 8 statistically significant factors as shown on this page.
For RQ3, we conducted an exit interview with 15 participants.
With their real-life circumstances, we found common themes by using affinity diagramming.
Surprisingly, there were many positively changed cases
so we summarized them in the paper.
Here are the summary of this presentation.
We need to emphasize this emotion change phenomenon and share this
with the research teams in the affective computing area.
Please consider making differentiating the emotion itself and
the changed emotion when using ESM.
Our research showed that emotion changes during ESM response tasks should not be overlooked.
We also revealed 8 contextual factors that are related to the emotion
change phenomenon and future research can use them to lower the
occurrence of emotion changes.
Thank you so much.
Please contact us when you are interested in this paper.