Hotline Bling: Chatbot M.A.X. that provides hotlines based on the user's mental health
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2021-02-12
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en
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Abstract
Anybody can go through a hard time. It has been estimated that 29% of
the people worldwide su er from mental health disorders at least once in
their lifetime (Abd-Alrazaq et al., 2019). By sharing your feelings, it could
lighten the negative state of mind a bit. Nevertheless, not everyone is able
to talk to a therapist. This includes several reasons, such as shame or lack
of money and/or time. Although mental health chatbots cannot function as
a replacement (yet), they can be useful as an additional tool. Because con-
versations about mental health can often be quite heavy, it is important a
chatbot is able to recognize the user's mental states (detect) and provide the
appropriate hotlines1 if needed (react). This thesis forms a pipeline2 with
the thesis of M. Reksoprodjo. Her thesis focused on the detection part, while
mine focused on the reaction. However, note that the actual implementa-
tion of the pipeline could be done in further research. The research question
concerned whether it was possible to build a working non-embodied chatbot
that detects SOS signals of poor mental health and is able to refer the user
to hotlines. Literature research was done and a proof-of-concept chatbot,
called M.A.X. (Mental AI Access), was created to answer the research ques-
tion. In addition, a web-application was made to deploy the chatbot on.
M.A.X. was reviewed along the following scienti c measures: e ciency, ef-
fectiveness, dialogue handling, anthropomorphism and esthetics. While the
chatbot gives hotlines when triggered to do so, further research is needed to
draw strong conclusions and answer the research question properly.
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Faculteit der Sociale Wetenschappen