From Words to Actions
Authors:   , Salmi Salim 
Published: November 2024

This dissertation explores the integration of NLP and ML techniques
to enhance the eﬀectiveness and eﬃciency of suicide prevention
helplines. By developing and evaluating AI-driven support tools, this
research aims to provide real-time assistance to counselors, improve
the quality of helpline services, and gain deeper insights into the
challenges faced by individuals in crisis.
Chapter 2 outlines the design and development of a content-based
recommender support system aimed at assisting counselors in a suicide
prevention helpline. The primary motivation was to address the
cognitive and emotional challenges faced by counselors during intense
chat conversations. By implementing a system that provides
suggestions based on previous successful interactions, the goal was to
investigate the potential for improved counselor eﬃcacy. Similar interactions
were found using averaged word vectors. The evaluation of this tool in a
simulated environment showed that the usability of the tool was good and the
retrieved suggestions were better than random suggestions, but tailored advice
from a human expert still had significantly more utility.
Building on the support tool developed in Chapter 2, Chapter 3
presents an evaluation of the eﬀectiveness through a randomized
controlled trial. The recommender system was improved using a deep-learning
method and tested in real-time counseling sessions to assess
its impact on counselors’ self-eﬃcacy and the quality of their responses.
While the tool did not significantly improve self-eﬃcacy scores, it was
frequently used in longer, more complex conversations, suggesting its
utility in challenging situations. The study highlighted the feasibility
of integrating AI-assisted tools in helpline services, paving the way for
future enhancements.
Chapter 4 focuses on evaluating various topic modeling methods to
analyze conversation data from mental health helplines. Traditional
methods like LDA were compared with newer techniques such as
BERTopic, which leverages sentence embeddings for better context
capture. The study found that BERTopic outperformed other methods
in terms of topic coherence and interpretability, especially when
applied to short, context-dependent texts typical of helpline conversations.
The insights gained from this analysis can help helplines
better understand the issues faced by their clients and improve their
services.
The aim of Chapter 5 is to identify changes in conversation topics on
a suicide prevention helpline during the COVID-19 pandemic. Using
BERTopic, the study analyzed chat data before and after the lockdown
measures were implemented. The results indicated significant shifts
in conversation topics, with an increased mention expressions of
gratitude towards counselors. There were decreases in mentions of
specific suicide plans, however, specifically helpseekers who lived
alone showed an increase in plans for suicide. These findings
underscore the impact of the pandemic on mental health and the
potential for monitoring of helpline conversations.
Chapter 6 explores the use of AI models to classify counselor and client
behaviors in the context of Motivational Interviewing (MI) during
helpline chats. By training models on a coded dataset of MI sessions,
the study aimed to automate the identification of counseling
techniques. The deep learning model BERTje showed high accuracy in
classifying MI behaviors, indicating its potential as a tool for providing
feedback to counselors. This automation could enhance the
monitoring of MI in online helplines and potentially boost adherence.
Finally, Chapter 7 investigates which counselor utterances contribute
to positive outcomes for help seekers using deep learning models.
By analyzing chat logs and help seeker self-assessments, the study
identified key behaviors of counselors that positively or negatively aﬀected
client wellbeing. Positive aﬃrmations and expressing involvement
were linked to improved scores, while the use of macros and premature
conversation endings had negative eﬀects. The study highlighted the
potential of ML to provide actionable insights for training counselors
and enhancing the eﬀectiveness of helpline conversations.