Changes in Mental State for Help-Seekers of Lifeline Australia’s Online Chat Service: Lexical Analysis Approach
Abstract BackgroundMental health challenges are escalating globally, with increasing numbers of individuals accessing crisis helplines through various modalities. Despite this growing demand, there is limited understanding of how crisis helplines benefit help-seekers over the...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-06-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2025/1/e63257 |
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| Summary: | Abstract
BackgroundMental health challenges are escalating globally, with increasing numbers of individuals accessing crisis helplines through various modalities. Despite this growing demand, there is limited understanding of how crisis helplines benefit help-seekers over the course of a conversation. Affective computing has the potential to transform this area of research, yet it remains relatively unexplored, partly due to the scarcity of available helpline data.
ObjectiveThis study aimed to explore the feasibility of using lexical analysis to track dynamic changes in the mental state of help-seekers during online chat conversations with a crisis helpline.
MethodsLexical analysis was conducted on 6618 deidentified online chat transcripts collected by Lifeline Australia between April and June 2023 using the validated Empath lexical categories of Positive Emotion, Negative Emotion, Suffering, and Optimism. Furthermore, 2 context-specific categories, Distress and Suicidality, were also developed and analyzed to reflect crisis support language. Correlation analyses evaluated the relationships between the 6 lexical categories. One-way ANOVAs assessed changes in each lexical category across 3 conversation phases (beginning, middle, and end). Trend analyses using regression modeling examined the direction and strength of changes in lexical categories across 9 overlapping conversation windows (20% size and 50% step overlap).
ResultsSignificant changes were observed across conversation phases. The context-specific categories showed the strongest improvements from the beginning to end phase of conversation, with a large reduction in Distress (ddd=d=d=d=rrRRRRRR
ConclusionsThis study demonstrates the feasibility of using lexical analysis to represent and monitor mental state changes during online crisis support interactions. The findings highlight the potential for integrating affective computing into crisis helplines to enhance service delivery and outcome measurement. Future research should focus on validating these findings and exploring how lexical analysis can be applied to improve real-time support to those in crisis. |
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| ISSN: | 2561-326X |