Large language model-facilitated interventions for enhancing psychological resilience in cyberbullying victims: Targeting self-esteem and self-compassion

doi:10.3724/SP.J.1041.2026.1751

Abstract

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

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.

References

[1] Batmaz H., Türk N., Kaya A., & Yıldırım M. (2023). Cyberbullying and cyber victimization: Examining mediating roles of empathy and resilience. Current Psychology, 42(35), 30959-30969. https://doi.org/10.1007/s12144-022-04134-3
[2] Bickmore, T. W., & Picard, R. W. (2005). Establishing and maintaining long-term human-computer relationships. ACM Transactions on Computer-Human Interaction, 12(2), 293-327. https://doi.org/10.1145/1073341.1073346
[3] Blanken T. F., Van Der Zweerde T., Van Straten A., Van Someren E. J., Borsboom D., & Lancee J. (2019). Introducing network intervention analysis to investigate sequential, symptom-specific treatment effects: A demonstration in co-occurring insomnia and depression. Psychotherapy and Psychosomatics, 88(1), 52-54. https://doi.org/10.1159/000495045
[4] Bluth K., Mullarkey M., & Lathren C. (2018). Self-compassion: A potential path to adolescent resilience and positive exploration. Journal of Child and Family Studies, 27(9), 3037-3047. https://doi.org/10.1007/s10826-018-1125-1
[5] Bonanno, G. A., & Diminich, E. D. (2013). Annual research review: Positive adjustment to adversity-trajectories of minimal-impact resilience and emergent resilience. Journal of Child Psychology and Psychiatry, 54(4), 378-401. https://doi.org/10.1111/jcpp.12021
[6] Bonanno G. A., Romero S. A., & Klein S. I. (2015). The temporal elements of psychological resilience: An integrative framework for the study of individuals, families, and communities. Psychological Inquiry, 26(2), 139-169. https://doi.org/10.1080/1047840X.2015.992677
[7] Borsboom, D., & Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91-121. https://doi.org/10.1146/annurev-clinpsy-050212-185608
[8] Campbell, A. (1976). Subjective measures of well-being. American Psychologist, 31(2), 117-124. https://doi.org/10.1037/0003-066X.31.2.117
[9] Carlbring P., Hadjistavropoulos H., Kleiboer A.,& Andersson, G.(2023). A new era in Internet interventions: The advent of ChatGPT and AI-assisted therapist guidance. Internet Interventions, 32, 100621. https://doi.org/10.1016/j.invent. 2023.100621
[10] Cuijpers P., Reijnders M., & Huibers M. J. (2019). The role of common factors in psychotherapy outcomes. Annual Review of Clinical Psychology, 15(1), 207-231. https://doi.org/10.1146/annurev-clinpsy-050718-095424
[11] Chaudhary P. K., Yalamati S., Palakurti N. R., Alam N., Kolasani S.,& Whig, P.(2024, July). Detecting and preventing child cyberbullying using generative artificial intelligence. In 2024 Asia Pacific Conference on Innovation in Technology (APCIT)2024.10206797
[12] Chen J., Yan L.,& Zhou, L.(2011). Reliability and validity of Chinese version of self-compassion scale. Chinese Journal of Clinical Psychology, 192011.06.006
[陈健, 燕良轼, 周丽华. (2011). 中文版自悯量表的信效度研究.中国临床心理学杂志, 19(6), 734-736.]
[13] Chiu, Y. Y., Sharma, A., Lin, I. W., & Althoff, T. (2024). A computational framework for behavioral assessment of LLM therapists. arXiv: 2401.00820.
[14] Chmitorz A., Kunzler A., Helmreich I., Tüscher O., Kalisch R., Kubiak T., .. Lieb, K.(2018). Intervention studies to foster resilience-A systematic review and proposal for a resilience framework in future intervention studies. Clinical Psychology Review, 59, 78-100. https://doi.org/10.1016/j.cpr. 2017.11.002
[15] Chu X. W., Fan C. Y., Liu Q. Q.,& Zhou, Z. K.(2018). Cyberbullying victimization and symptoms of depression and anxiety among Chinese adolescents: Examining hopelessness as a mediator and self-compassion as a moderator. Computers in Human Behavior, 86, 377-386. https://doi.org/10.1016/j.chb.2018.04.039
[16] Costantini G., Epskamp S., Borsboom D., Perugini M., Mottus R., Waldorp L. J.,& Cramer, A. O. J.(2015). State of the art personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality, 54, 13-29. https://doi.org/10.1016/j.jrp.2014.07.003
[17] Cross D., Shaw T., Hadwen K., Cardoso P., Slee P., Roberts C., .. Barnes A. (2016). Longitudinal impact of the Cyber Friendly Schools program on adolescents’ cyberbullying behavior. Aggressive Behavior, 42(2), 166-180. https://doi.org/10.1002/ab.21609
[18] Demszky D., Yang D., Yeager D. S., Bryan C. J., Clapper M., Chandhok S., .. Pennebaker J. W. (2023). Using large language models in psychology. Nature Reviews Psychology, 2(11), 688-701. https://doi.org/10.1038/s44159-023-00241-5
[19] Epskamp S., Borsboom D., & Fried E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195-212. https://doi.org/10.3758/s13428-017-0862-1
[20] Fishbein J. N., Haslbeck J.,& Arch, J. J.(2023). Network intervention analysis of anxiety-related outcomes and processes of acceptance and commitment therapy (ACT) for anxious cancer survivors. Behaviour Research and Therapy, 162, 104266. https://doi.org/10.1016/j.brat.2023.104266
[21] Fitzpatrick K. K., Darcy A., & Vierhile M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785
[22] Foody M., Samara M.,& Carlbring, P.(2015). A review of cyberbullying and suggestions for online psychological therapy. Internet Interventions, 22015.05.002
[23] Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56(3), 218-226. https://doi.org/10.1037/0003-066X.56.3.218
[24] Gong X., Xie X., Xu R.,& Luo, Y.(2010). Psychometric properties of the Chinese versions of DASS-21 in Chinese college students. Chinese Journal of Clinical Psychology, 182010.04.020
[龚栩, 谢熹瑶, 徐蕊, 罗跃嘉. (2010). 抑郁-焦虑-压力量表简体中文版(DASS-21)在中国大学生中的测试报告.中国临床心理学杂志, 18(4), 443-446.]
[25] Haslbeck J. M.B., Borsboom, D., & Waldorp, L. J.(2021). Moderated network models. Multivariate Behavioral Research, 56 2019.1677207
[26] He, C. (2025, January 10). Ministry of Public Security: In 2024, public security organs handled more than 8, 600 cases of cyber violence. China Youth Daily. https://s.cyol.com/articles/2025-01/10/content_Q42PLECW.html
[何春中. (2025-1-10). 公安部: 2024年公安机关共办理网络暴力案件8600余起.中国青年报.]
[27] Heatherton, T. F., & Polivy, J. (1991). Development and validation of a scale for measuring state self-esteem. Journal of Personality and Social Psychology, 60(6), 895-910. https://doi.org/10.1037/0022-3514.60.6.895
[28] Henry, J. D., & Crawford, J. R. (2005). The short-form version of the Depression Anxiety Stress Scales (DASS-21): Construct validity and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 44(2), 227-239. https://doi.org/10.1348/014466505X29657
[29] Hinduja S.,& Patchin, J. W.(2017). Cultivating youth resilience to prevent bullying and cyberbullying victimization. Child Abuse & Neglect, 73, 51-62. https://doi.org/10.1016/j.chiabu.2017.09.010
[30] Jacobs N. C., Völlink T., Dehue F., & Lechner L. (2014). Online Pestkoppenstoppen: Systematic and theory-based development of a web-based tailored intervention for adolescent cyberbully victims to combat and prevent cyberbullying. BMC Public Health, 14(1), 1-16. https://doi.org/10.1186/1471-2458-14-396
[31] Jain G., Pareek S.,& Carlbring, P.(2024). Revealing the source: How awareness alters perceptions of AI and human-generated mental health responses. Internet Interventions, 36, 100745. https://doi.org/10.1016/j.invent.2024.100745
[32] Kaplan, H. B. (1980). Deviant behavior in defense of self. American Journal of Sociology, 87(5). https://doi.org/10.1086/227584 .
[33] Kazdin, A. E. (2019). Annual research review: Expanding mental health services through novel models of intervention delivery. Journal of Child Psychology and Psychiatry, 60(4), 455-472. https://doi.org/10.1111/jcpp.12937
[34] Kishimoto T., Hao X., Chang T.,& Luo, Z.(2025). Single online self-compassion writing intervention reduces anxiety: With the feedback of ChatGPT. Internet Interventions, 39, 100810. https://doi.org/10.1016/j.invent.2025.100810
[35] Kowalski R. M., Giumetti G. W., & Feinn R. S. (2023). Is cyberbullying an extension of traditional bullying or a unique phenomenon? A longitudinal investigation among college students. International Journal of Bullying Prevention, 5(3), 227-244. https://doi.org/10.1007/s42380-022-00127-1
[36] Kumpfer, K. L. (1999). Factors and processes contributing to resilience: The resilience framework. In M. D. Glantz & J. L. Johnson (Eds.), Resilience and development: Positive life adaptations(pp. 179-224). Kluwer Academic/Plenum Publishers. https://doi.org/10.1007/0-306-47167-1_9
[37] Lee H., Bozek D., Park J.,& Lee, J. M.(2024). The role of perceived social support and resilience in college cyberbullying victimization and mental health: A serial mediation model. Journal of College Student Mental Health, 392024.2407431
[38] Li Y., He W., Zhang X., Guo Q.,& Ma, B.(2014). Correlation between psychological capital and well-being index of parental rearing style, coping style of college students. Chinese Journal of School Health, 352014.01.051821
[李艳, 何畏, 张贤, 郭巧红, 马波. (2014). 大学生心理资本与父母教养方式应对方式幸福感指数的相关性.中国学校卫生, 35(1), 128-131.]
[39] Lin Y., Sun R., Zhang J., Xu K., & Yu S. (2025). Discussion topics, sentiments, and usage intentions: A three-dimensional perspective of public attitudes toward large language models. Journal of Psychological Science, 48(6), 1359-1369. https://doi.org/10.16719/j.cnki.1671-6981.20250606.
[林原, 孙睿桐, 张君, 许侃, 于硕. (2025). 议题、情绪和使用倾向: 大语言模型公众态度的三维视角——基于网络评论的计算文本分析.心理科学, 48(6), 1359-1369.]
[40] Lin, Z. (2025). Large language models as psychological simulators: A methodological guide. arXiv:2506.16702.
[41] Lock S., Rees C. S., & Heritage B. (2020). Development and validation of a brief measure of psychological resilience: The state trait assessment of resilience scale. Australian Psychologist, 55(1), 10-25. https://doi.org/10.3758/s13428-017-0862-1
[42] Marshall S. L., Parker P. D., Ciarrochi J., Sahdra B., Jackson C. J.,& Heaven, P. C. L.(2015). Self-compassion protects against the negative effects of low self-esteem: A longitudinal study in a large adolescent sample. Personality and Individual Differences, 74, 116-121. https://doi.org/10.1016/j.paid.2014.09.013
[43] Milosevic T., Verma K., Carter M., Vigil S., Laffan D., Davis B., & O’Higgins Norman J. (2023). Effectiveness of artificial intelligence-based cyberbullying interventions from youth perspective. Social Media and Society, 9(1), 20563051221147325. https://doi.org/10.1177/20563051221147325
[44] Miner A. S., Milstein A., Schueller S., Hegde R., Mangurian C.,& Linos, E.(2016). Smartphone-based conversational agents and responses to questions about mental health, interpersonal violence, and physical health. JAMA Internal Medicine, 1762016.0400
[45] Muris P., Meesters C., Pierik A., & de Kock B. (2016). Good for the self: Self-compassion and other self-related constructs in relation to symptoms of anxiety and depression in non-clinical youths. Journal of Child and Family Studies, 25(2), 607-617. https://doi.org/10.1007/s10826-015-0235-2
[46] Navarro R., Ruiz-Oliva R., Larrañaga E., & Yubero S. (2015). The impact of cyberbullying and social bullying on optimism, global and school-related happiness and life satisfaction among 10-12-year-old schoolchildren. Applied Research in Quality of Life, 10(1), 15-36. https://doi.org/10.1007/s11482-013-9292-0
[47] Neff, K. D. (2003). Self-compassion: An alternative conceptualization of a healthy attitude toward oneself. Self and Identity, 2(2), 85-101. https://doi.org/10.1080/15298860309032
[48] Neff, K. D. (2011). Self-compassion, self-esteem, and well-being. Social and Personality Psychology Compass, 5(1), 1-12. https://doi.org/10.1111/j.1751-9004.2010.00330.x
[49] Newman M. E.J. (2010). Networks: An introduction Oxford University Press An introduction. Oxford University Press.
[50] Newman M. G., Szkodny L. E., Llera S. J.,& Przeworski, A.(2011). A review of technology-assisted self-help and minimal contact therapies for anxiety and depression: Is human contact necessary for therapeutic efficacy? Clinical Psychology Review, 312010.09.008
[51] Opsahl T., Agneessens F., & Skvoretz J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245-251. https://doi.org/10.1016/j.socnet.2010.03.006
[52] Orth, U., & Robins, R. W. (2013). Understanding the link between low self-esteem and depression. Current Directions in Psychological Science, 22(6), 455-460. https://doi.org/10.1177/0963721413492763
[53] Patchin J. W.,& Hinduja, S.(2015). Measuring cyberbullying: Implications for research. Aggression and Violent Behavior, 23, 69-74. https://doi.org/10.1016/j.avb.2015.05.013
[54] Provoost S., Lau H. M., Ruwaard J., & Riper H. (2017). Embodied conversational agents in clinical psychology: A scoping review. Journal of Medical Internet Research, 19(5), e151. https://doi.org/10.2196/jmir.6553
[55] Qian, X. (2024). Reliability analysis and Rash model measurement of the state self-esteem scale in a Chinese sample. Psychological Monthly, 19(23), 87-89. https://doi.org/10.19738/j.cnki.psy.2024.23.023
[钱旭宏. (2024). 状态自尊量表在中国样本中的信效度分析与Rash模型测评.心理月刊, 19(23), 87-89.]
[56] Raile, P. (2024). The usefulness of ChatGPT for psychotherapists and patients. Humanities and Social Sciences Communications, 11(1), 47. https://doi.org/10.1057/s41599-023-02567-0
[57] Rémond J. J., Kern L.,& Romo, L.(2014). A cyberbullying study: Analysis of cyberbullying, comorbidities and coping mechanisms. L'encéphale, 41(4), 287-294. https://doi.org/10.1016/j.encep.2014.08.003
[58] Santos D.,Mateos-Pérez, E., Cantero, M., & Gámez-Guadix, M.(2021). Cyberbullying in adolescents: Resilience as a protective factor of mental health outcomes. Cyberpsychology, Behavior, and Social Networking, 242020.0337
[59] Shapka J. D.,& Maghsoudi, R.(2017). Examining the validity and reliability of the cyber-aggression and cyber-victimization scale. Computers in Human Behavior, 69, 10-17. https://doi.org/10.1016/j.chb.2016.12.015
[60] Sontag-Padilla,L., Dunbar, M. S., Ye, F., Kase, C., Fein, R., Abelson, S., .. Stein, B. D.(2018). Strengthening college students’ mental health knowledge, awareness, and helping behaviors: The impact of active minds, a peer mental health organization. Journal of the American Academy of Child & Adolescent Psychiatry, 572018.03.019
[61] Sourander A., Klomek A. B., Ikonen M., Lindroos J., Luntamo T., Koskelainen M., .. Helenius, H.(2010). Psychosocial risk factors associated with cyberbullying among adolescents: A population-based study. Archives of General Psychiatry, 672010.79
[62] Tao Y., Viberg O., Baker R. S., & Kizilcec R. F. (2024). Cultural bias and cultural alignment of large language models. PNAS Nexus, 3(9), 346. https://doi.org/10.1093/pnasnexus/pgae346
[63] Veselska Z., Geckova A. M., Orosova O., Gajdosova B., van Dijk J. P., & Reijneveld S. A. (2009). Self-esteem and resilience: The connection with risky behavior among adolescents. Addictive Behaviors, 34(3), 287-291. https://doi.org/10.1016/j.addbeh.2008.11.005
[64] Wang M. C., Deng J., Shou Y., & Sellbom M. (2022). Cross-cultural examination of psychopathy network in Chinese and U.S. prisoners. Journal of Psychopathology and Behavioral Assessment, 44(3), 620-635. https://doi.org/10.1007/s10862-022-09960-0
[65] Wen Z., Wang Y., Ma P.,& Meng, J.(2024). The influence relationship among variables and types of multiple influence factors working together. Acta Psychologica Sinica, 562024.01462
[温忠麟, 王一帆, 马鹏, 孟进. (2024). 变量之间的影响关系和多重影响因素的共同作用类型.心理学报, 56(10), 1462-1471.]
[66] Wu X., Wang P., Yang W., Ding X. (2023, June 20). 65.3% of the respondents said that they or those around them had experienced cyberviolence. China Youth Daily. https://news. cyol.com/gb/articles/2023-06/20/content_zx5pJpiYlq.html
[吴欣宇, 王品芝, 杨万淑, 丁香雪钰. (2023-6-20). 65.3%受访青年表示自己或周围人遭遇过网络暴力.中国青年报.]
[67] Xie X., Zheng H., Ju K., Xiao B., Liu J.,& Shapka, J. D.(2022). Examining the validity and reliability of the short form of cyberbullying and cybervictimization (CAV) scale. Chinese Journal of Clinical Psychology, 302022.05.029
[解晓龙, 郑宏, 鞠康, 肖博文, 刘俊升, Jennifer D. Shapka. (2022). 简式网络欺凌与网络受害量表中文版的信效度检验.中国临床心理学杂志, 30(5), 1155-1159.]
[68] Ybarra, M. L. (2004). Linkages between depressive symptomatology and Internet harassment among young regular Internet users. CyberPsychology & Behavior, 7(2), 247-257. https://doi.org/10.1089/109493104323024500