大语言模型干预网络欺凌受害者心理韧性: 以自尊自悯为靶点

doi:10.3724/SP.J.1041.2026.1751

摘要/Abstract

摘要： 本研究以自尊和自悯为靶点, 探索大语言模型(Large Language Models, LLM)在干预网络欺凌受害者心理韧性中的效果与作用路径。研究1 (干预效果检验)采用随机对照设计, 比较自尊-自悯对话、心理教育对话和心理教育阅读三组的干预效果。结果显示两组对话式LLM均显著提升了心理韧性。研究2 (作用路径分析)进一步采用网络干预分析揭示干预作用路径, 发现自尊-自悯对话干预除了可以直接提高心理韧性外, 还可以靶向孤立感、正念和社交维度自尊, 通过自尊自悯多维度的相互作用, 提升干预效果。本研究首次聚焦自尊与自悯路径, 基于LLM构建具备共情交互能力的对话干预平台, 为网络欺凌受害者提供个性化心理支持, 并为智能化心理干预工具的开发提供理论依据与实践路径。

关键词: 大语言模型, 网络欺凌, 心理韧性, 自尊, 自悯

Abstract: Cyberbullying is a prevalent form of online aggression that threatens mental health, especially among adolescents and young adults. Compared with traditional bullying, it is more persistent, less visible, and harder to control, often leading to anxiety, depression, stress, social withdrawal, and reduced well-being. Psychological resilience is an important protective resource that helps victims coping with adversity. Among the psychological factors related to resilience, self-esteem and self-compassion are especially relevant because they shape how individuals evaluate themselves and respond to suffering. Existing interventions for cyberbullying victims often lack personalization, emotional interaction, and scalability. Against this background, the present research examined whether large language model (LLM)-facilitated dialogue interventions could enhance resilience in cyberbullying victims, and whether an intervention targeting self-esteem and self-compassion would be more effective than general psychoeducation.
The research consisted of two independent studies. Study 1 used a three-arm randomized controlled design with 59 participants who reported previous cyberbullying experiences. They were randomly assigned to a self-esteem/self-compassion dialogue condition, a psychoeducational dialogue condition, or a psychoeducational reading control condition. Participants completed baseline measures, received a 10- to 15-minute intervention, and then completed posttest assessments. Primary outcomes were state resilience and trait resilience, and secondary outcomes included anxiety, depression, stress, and well-being. Study 2 recruited a new sample of 105 participants and compared the two dialogue conditions only. In addition to the outcomes used in Study 1, Study 2 assessed state self-esteem and self-compassion and applied network intervention analysis to examine the mechanisms underlying the two interventions. Both studies used a WeChat mini-program connected to ChatGPT-4o, and all procedures were completed with informed consent and ethical approval.
In Study 1, repeated-measures analyses showed that both dialogue-based LLM interventions improved resilience-related outcomes more clearly than the reading control. For state resilience, the time × group interaction was significant, indicating that change over time differed across conditions. After baseline state resilience was controlled, posttest state resilience in the self-esteem/self-compassion group was significantly higher than that in the reading group. Both dialogue groups showed significant within-group increases in state resilience, but the self-esteem/self-compassion group showed the largest improvement. For trait resilience, only the time effect was significant, suggesting a general pre-post increase without a clear differential group effect. Regarding secondary outcomes, anxiety and depression decreased over time across groups. Stress and well-being showed significant time × group interactions: stress significantly decreased only in the self-esteem/self-compassion group, whereas well-being significantly increased in both dialogue groups but not in the reading group. In Study 2, the overall intervention pattern was broadly consistent with Study 1, supporting the stability of the main findings. Network intervention analysis further showed that, compared with the psychoeducational dialogue, the self-esteem/self-compassion dialogue was uniquely linked to lower isolation, higher mindfulness, higher social self-esteem, and a direct increase in state resilience. These nodes formed a pathway through which changes in self-esteem and self-compassion jointly contributed to resilience enhancement.
Taken together, the findings showed that brief LLM-facilitated dialogue interventions can improve the psychological functioning of cyberbullying victims, and that a targeted intervention focusing on self-esteem and self-compassion has greater specificity in both outcomes and mechanisms. The results suggest that resilience, particularly state resilience, can be strengthened in short-term digital intervention contexts. They also indicate that self-esteem and self-compassion contribute to resilience through interrelated processes involving social self-evaluation, mindfulness, and reduced isolation. In practical terms, LLM-based interventions may provide scalable and responsive support for individuals affected by cyberbullying.

Key words: large language models, cyberbullying, psychological resilience, self-esteem, self-compassion

岸本鹏子, 郝熙鸣, 艾尼卡尔·艾斯卡尔, 夏雨飞, 白麒钰. (2026). 大语言模型干预网络欺凌受害者心理韧性: 以自尊自悯为靶点. 心理学报, 58(9), 1751-1765.

KISHIMOTO Tomoko, HAO Ximing, ASKAR Ankar, XIA Yufei, BAI Qiyu. (2026). Large language model-facilitated interventions for enhancing psychological resilience in cyberbullying victims: Targeting self-esteem and self-compassion. Acta Psychologica Sinica, 58(9), 1751-1765.

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