Changes in Mental State for Help-Seekers of Lifeline Australia’s Online Chat Service: Lexical Analysis Approach
Bibliographic record
Abstract
Background: Mental 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. Objective: This 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. Methods: Lexical 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). Results: Significant 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 (d=0.79) and a moderate reduction in Suicidality (d=0.49). The most frequently occurring terms representing Distress were "hard," "bad," and "down," and for Suicidality were "suicide," "stop," and "hurt." The negatively framed Empath categories also significantly reduced, with moderate effect sizes for Suffering (d=0.49) and Negative Emotion (d=0.39). There were also significant but small reductions in the positively framed Empath categories of Positive Emotion (d=0.15) and Optimism (d=0.07) from the beginning to end phase of conversation. Correlation coefficients indicated the lexical categories captured related but distinct constructs (r=.34 to r=0.82). Trend analyses revealed a consistent downward trajectory across most lexical categories. Distress showed the steepest decline (slope=-0.15, R²=0.97), followed by Suffering (slope=-0.11, R²=0.96), Negative Emotion (slope=-0.10, R²=0.69), and Suicidality (slope=-0.06, R²=0.88). Positive Emotion showed a slight negative trend (slope=-0.04, R²=0.54), while Optimism remained relatively stable across the conversation windows (slope=0.01, R²=0.13). Conclusions: This 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".