Fu Ziru, Hsu Yu Cheng, Chan Christian Shaunlyn, Yip Paul Siu Fai
Department of Social Work and Social Administration, Faculty of Social Sciences, Faculty of Social Sciences, University of Hong Kong, Hong Kong, China (Hong Kong).
The Hong Kong Jockey Club Centre for Suicide Research and Prevention, University of Hong Kong, 5 Sassoon Rd, Sandy Bay, Hong Kong, 999077, China (Hong Kong), 852 2831 5232.
J Med Internet Res. 2025 Sep 5;27:e75091. doi: 10.2196/75091.
Online text-based counseling services are becoming increasingly popular. However, their text-based nature and anonymity pose challenges in tracking and understanding shifts in help-seekers' emotional experience within a session. These characteristics make it difficult for service providers to tailor interventions to individual needs, potentially diminishing service effectiveness and user satisfaction.
This study aimed to identify distinct within-session sentiment trajectories among help-seekers in online text-based counseling and examine key variables associated with trajectory membership.
A total of 6207 counseling sessions were randomly extracted from an online text-based counseling service in Hong Kong. A latent class trajectory analysis of help-seekers' in-session sentiment was conducted using a growth mixture model (GMM) to identify latent groups of help-seekers exhibiting specific sentiment trajectories. Sentiment scores of help-seeker messages, labeled by ChatGPT, served as the primary variable for trajectory modeling. Subsequently, a multinomial logistic regression was performed to identify variables associated with class membership.
The GMM identified 3 distinct sentiment trajectories as the best fit: (1) steady improvement (1171/6207, 18.9%), (2) deterioration (1119/6207, 18.0%), and (3) dip-then-rebound (3917/6207, 63.1%). Compared with the Dip-Then-Rebound Class, help-seekers in the Deterioration Class were more likely to report suicidal ideation (OR=1.28, 95% CIs 1.07-1.52, P=.006), present with family (OR=1.56, 95% CIs 1.19-2.08, P=.002) or physical health-related concerns (OR=1.67, 95% CIs 1.02-2.74, P=.04), have an unknown gender status (OR=1.32, 95% CIs 1.04-1.67, P=.02), access the service through the anonymous channel (OR=1.30, 95% CIs 1.03-1.63, P=.03), depart from the session prematurely (OR=9.76, 95% CIs 8.33-11.36, P<.001), and have shorter session durations (OR=0.77, 95% CIs 0.71-0.84, P<.001).
We identified 3 distinct trajectories of help-seekers' in-session sentiment. Identifying the most likely trajectory at an early stage in the session could potentially help counselors adjust their approaches, thereby improving the effectiveness of text-based counseling and enhancing help-seeker satisfaction.
基于文本的在线咨询服务越来越受欢迎。然而，其基于文本的性质和匿名性给追踪和理解求助者在咨询过程中的情绪体验变化带来了挑战。这些特征使得服务提供者难以根据个体需求调整干预措施，可能会降低服务效果和用户满意度。
本研究旨在识别基于文本的在线咨询中求助者在咨询过程中不同的情绪轨迹，并研究与轨迹类别相关的关键变量。
从香港一项基于文本的在线咨询服务中随机抽取6207次咨询会话。使用增长混合模型（GMM）对求助者咨询过程中的情绪进行潜在类别轨迹分析，以识别表现出特定情绪轨迹的求助者潜在群体。由ChatGPT标注的求助者消息的情绪得分作为轨迹建模的主要变量。随后，进行多项逻辑回归以识别与类别归属相关的变量。
GMM确定了3种不同的情绪轨迹为最佳拟合：（1）稳步改善（1171/6207，18.9%），（2）恶化（1119/6207，18.0%），以及（3）先下降后反弹（3917/6207，63.1%）。与先下降后反弹类别相比，恶化类别中的求助者更有可能报告自杀意念（OR=1.28，95%CI 1.07-1.52，P=.006），提及家人（OR=1.56，95%CI 1.19-2.08，P=.002）或身体健康相关问题（OR=1.67，95%CI 1.02-2.74，P=.04），性别状况未知（OR=1.32，95%CI 1.04-1.67，P=.02），通过匿名渠道使用该服务（OR=1.30，95%CI 1.03-1.63，P=.03），提前结束咨询（OR=9.76，95%CI 8.33-11.36，P<.001），且咨询时长较短（OR=0.77，95%CI 0.71-0.84，P<.001）。
我们识别出了求助者在咨询过程中3种不同的情绪轨迹。在咨询早期识别最可能的轨迹可能有助于咨询师调整他们的方法，从而提高基于文本的咨询效果并提升求助者满意度。
