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Machine learning analysis of suicide prevention helpline chats reveals that positive affirmations and involvement from helpers improve help seeker outcomes. Conversely, premature chat endings and automated responses negatively impact users.

Keywords:
BERTLLMartificial intelligencebidirectional encoder representations from transformersclassificationconversationsexplainable AIhelplineinterpretable AIlarge language modelsmachine learningnatural language processingsuicidalitysuicidesuicide helplinesuicide prevention

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Area of Science:

  • Psychology
  • Computer Science
  • Public Health

Background:

  • Optimal care in suicide prevention helplines requires understanding factors influencing help seeker outcomes.
  • Text-based chat services generate substantial data for large-scale analysis.

Purpose of the Study:

  • To train a machine learning model to predict chat outcomes in suicide prevention helplines.
  • To identify specific counselor utterances impacting model predictions and help seeker scores.

Main Methods:

  • Trained a machine learning classification model on chat conversations from 6903 help seekers (August 2021-January 2023).
  • Utilized machine learning text analysis to predict help seeker scores on suicidality factors (e.g., hopelessness, will to live).
  • Employed two interpretation approaches to identify impactful helper messages within the chat data.

Main Results:

  • Helper's positive affirmations and expressions of involvement positively correlated with improved help seeker scores.
  • Use of automated responses (macros) and premature chat termination negatively affected help seeker outcomes.
  • The machine learning model successfully predicted chat outcomes based on conversation content.

Conclusions:

  • Insights suggest improving helpline chats through an evocative style, including questions, affirmations, and practical advice.
  • Machine learning demonstrates significant potential for analyzing helpline chat data to enhance support.
  • Understanding specific communication elements can optimize care for individuals in crisis.