Hi.
I'm Yi-Chieh Lee.
I'm going to present our research, Designing Chatbot
as a Mediator for Promoting Deep USD Self-Disclosure
to a Real Mental Health Professional.
This work is collaborated with NTT researcher, Naomi Yamashita
and my advisor, Yun Huang.
In this research, we are interested in user's
self-disclosure behaviors with their chatbot
to expose how the chatbots could encourage humans gradually
disclose their personal information, thoughts,
feelings, and even fears to others.
Due to a time limitation, we offer the full versions
on personal website.
I will share a link at the end of this presentation.
This figure is playing a scenario of our study.
There are two main parts in this study.
In the first part of the study, we
are interested in how to design chatbot to elicit the user's
deep self-disclosure.
As I mentioned above, in the health care application,
it may require users to interact with the system
over a long period of time.
Now through a long-term interaction with the chatbot,
we are interested in if a human could follow
through a relationship and the self-disclosure
deeply with the chatbot.
With the second part, we aim to develop
a chatbot that plays as a mediator between users
and the human supported.
Thus, after collecting users' deep self-disclosure,
we are wondering if a chatbot could serve as a mediator
to facilitate people self-disclosure to a mental
health professional.
First, we investigate how participants
question their self-disclosure with a human expert
through a chatbot.
And we follow experts what factors will
affect users' sharing behavior.
We have three conditions in these studies,
which come through in different level
of chatbot self-disclosure.
So that is from a no self-disclosure
to a high self-disclosure.
We want to see if the different level
of the chatbot self-disclosure could
elicits user's deeper self-disclosure and share
with a third party.
The conversation flow follows most of the chatbot designed
for health care.
Most of the [?] usually start from greeting
and then jump to a guidance or treatment,
which is similar to the ND condition in our study.
We modify this design by adding small talk before going
to a sensitive subject.
And we control the chatbot self-disclosure level
to explore how it will affect users' self-disclosure.
In the second part, the chatbot will introduce a doctor
or say the mental health professionals
to the participants.
And then suggest to them to share their self-disclosure
with their doctor.
And then they are allowed to modify their prior response
before sharing.
This is our study result. We found
that most of the responses were shared.
Only 8% of them not shared with their doctor,
which means that chatbot can effectively
mediate users' deeper self-disclosure
through a mental health professional.
However, we found that there were
some users decide to add or delete their prior answers.
Those users tend to reduce [? all ?]
details, such as their thoughts and feelings,
especially for ND group.
Users also did not like to share their thoughts which
may related to others, such as families or friends.
However, some participants add more information
before sharing with their doctor.
There are two main reasons why they add the information.
As you can see from the slides, the first reason
is because they want to help the doctor
to understand their response.
And they also maybe change their thoughts
when they saw those contents again.
For our interview, we can see chatbot
was one of the important factors mentioned by the participant.
Thus, we present our survey results
of participants trust in the chatbot
and MHP at different stages.
Based on these figures, you can find
that sharing data with doctor did not decrease
users' trust in chatbot.
However, we can see that when you validate
users' trust in a doctor, if these members has the highest
level of the trust, the reason is as the quote.
The high self-disclosure chatbot could
be used to convince users to trust the third parties.
Here, I give three insight for our study.
The high self-disclosure chatbot is
designed to connect the participants
with their doctor, which may help lower
the barriers of sharing this deep self-disclosure.
However, ethical issues may be risked here.
This finding also implies a potential risk for the users
to overshare their private information
with such a their chatbot.
This may cause users to disclose their vulnerabilities, which
may be dangerous if they are abused.
Leveraging the bonding between the professional image
and the chatbot need further study.
Overtrust in professional image may result in users
over estimating a chatbot's efficacy.
Finally, this is a limitation for this study.
Although participants has a deeper self-disclosure
and are willing to share it with a doctor,
this also increased their expectation
on getting feedback from the doctor.
But we did not address this impact in the study.
Therefore, in our future study, we are going to explore
the effect of integrating users' human support in human chatbot
interaction to give guidance, influence users' behaviors
and experience.
Thank you for your listening today.
This is my presentation.
If you want to see the full version,
please reach my personal website.
We provide a full version of video on my website.
If you have any questions, please
feel free to leave your message or send an email to me.
Thank you.