Background: Cyberbullying is often conceptualised as traditional bullying amplified by technology, yet it remains unclear whether digital contexts alter its psychological impact. This study examined whether cyberbullying victimisation shows different patterns of association with mental health outcomes compared with traditional bullying victimisation. Methods: A cross-sectional survey of 716 university students in Singapore was analysed using structural equation modelling. Cyberbullying was specified as a higher-order latent construct with three subdimensions (verbal/written, visual/sexual, and social-exclusion), whereas traditional bullying was modelled as a single latent factor. Models examined direct and indirect pathways from victimisation to depression, anxiety, and post-traumatic stress via self-esteem and peer attachment. Results: When modelled alone, cyberbullying victimisation was associated with higher depression, anxiety, and post-traumatic stress, both directly and indirectly through reduced self-esteem and peer attachment. When traditional bullying victimisation was added as a correlated latent predictor to the model, the indirect pathways for cyberbullying victimisation became non-significant. It retained only a unique direct association with anxiety, whereas traditional bullying victimisation accounted for most of the shared and indirect variance in depression and post-traumatic stress. Conclusions: Cyberbullying and traditional bullying victimisation share core psychosocial mechanisms but diverge when their shared variance is modelled. Traditional bullying victimisation shows broad emotional associations mediated by self-esteem and peer attachment, whereas cyberbullying’s unique effects are concentrated on anxiety. This pattern suggests that digital affordances such as anonymity may heighten uncertainty and perceived loss of control. Intervention efforts should address shared psychosocial harms while also helping individuals manage anxiety and uncertainty in online settings.

This study explores sex differences in the relationship between childhood cyberbullying victimization (CCBV) and young adult sexual assault experiences and the potential moderating roles of childhood parent monitoring and deviant peer association on the relationship. A total of 356 college students aged 19-25 in the US participated in the online survey. The results indicated that CCBV was associated with an increased risk of college sexual assault victimization for both males and females. A significant moderating effect of childhood parental monitoring and deviant peer association was found between the association between CCBV and college sexual assault victimization for female students only.

Qualitative coding is a demanding yet crucial research method in the field of Human-Computer Interaction (HCI). While recent studies have shown the capability of large language models (LLMs) to perform qualitative coding within theoretical frameworks, their potential for collaborative human-LLM discovery and generation of new insights beyond initial theory remains underexplored. To bridge this gap, we proposed CHALET , a novel approach that harnesses the power of human-LLM partnership to advance theory-driven qualitative analysis by facilitating iterative coding, disagreement analysis, and conceptualization of qualitative data. We demonstrated CHALET ’s utility by applying it to the qualitative analysis of conversations related to mental-illness stigma, using the attribution model as the theoretical framework. Results highlighted the unique contribution of human-LLM collaboration in uncovering latent themes of stigma across the cognitive, emotional, and behavioral dimensions. We discuss the methodological implications of the human-LLM collaborative approach to theory-based qualitative analysis for the HCI community and beyond.

With the rising number of maltreatment recurrence episodes in recent years and cultural differences in understanding child maltreatment across countries, the current study aims to comprehensively analyze the existing literature related to child maltreatment recurrence in Korea, including the definition, type of studies, prevalence, and risk/protective factors. The present review systematically searched for records until February 29, 2024, which yielded a total of 16 articles. The results showed that the definitions frequently included the keywords, such as “same victim child,” “same perpetrator,” “family/relatives as the perpetrator,” and “victim child aged below 17 or 18.” While the overall prevalence of child maltreatment re-report was 10.83%, the re-substantiation or recurrence rate was 9.00%. Additionally, maltreatment recurrence rate was the highest after more than 2 years of the first maltreatment report (10.51%). Some of the risk and protective factors included the physical abuse allegation, parental alcohol abuse, intimate partner violence, removal of children, out-of-home placement, longer provision of child welfare services, and follow-up monitoring service. Based on these findings, the present review suggests updating the definition of maltreatment recurrence to be more child-centered, increasing monthly stipends to secure more foster homes, and providing evidence-based post-permanency services.

Young persons with disabilities (YPWDs) are disproportionately targeted by bias-based bullying due to stigma, perceived difference, and systemic ableism. While global literature documents this risk, there is limited qualitative research from non-Western contexts that center the lived experiences of YPWDs. This study addresses that gap by exploring the retrospective narratives of 22 adults in Singapore who experienced bias-based bullying during their schooling years. Using phenomenological research design and inductive thematic analysis, the study uncovers how bias-based bullying was experienced not as isolated incidents but as chronic, intersecting forms of harm, including verbal taunts, physical aggression, social exclusion, and cyberbullying. These experiences reinforced feelings of inferiority and altered participants’ sense of belonging and identity during formative years. Findings highlight the lasting emotional toll of bias-based bullying, including mental health struggles, school disengagement, and diminished self-esteem, with effects persisting into adulthood. Yet, several participants also described processes of resistance and identity reclamation, reframing their disabilities as strengths and developing resilience over time. The study contributes to growing calls for inclusive, culturally grounded approaches to disability, education, and anti-bullying interventions. It urges schools to move beyond individual behavior management to address the structural and cultural forces that perpetuate exclusion. This includes examining institutional practices, increasing disability visibility and representation, and co-creating strategies with YPWDs. In centering the voices of disabled individuals in Singapore, the study affirms their expertise in shaping responses to bias-based bullying and offers critical insight into the emotional and developmental costs of ableism across the life course.

Cyberbullying is a pervasive problem in online environments, causing substantial psychological harm to victims. Although bystander intervention has proven effective in mitigating its impact, motivating bystanders to engage in direct intervention remains a persistent challenge. Studies have suggested that difficulties in intervention skills and defending self-efficacy hinder bystanders from initiating direct intervention. To address this challenge, we introduced EmojiGen, an AI intervention tool designed to empower bystanders for direct intervention. EmojiGen enabled users to simply select an emoji as an intention clue, which subsequently combined the cyberbullying context to generate responses. In a between-subjects experiment involving 90 participants on a custom-built social media platform, we found that EmojiGen significantly increased the frequency of direct bystander interventions, both in supporting victims and in confronting perpetrators, driven by different factors. EmojiGen also increased the sense of knowing how to help and defending self-efficacy, while reducing perceived workload and anxiety associated with initiating intervention. The study contributed to the CSCW community through offering an effective direct bystander intervention method and providing design implications for future cyberbullying interventions.

Mental-illness stigma is a persistent social problem, hampering both treatment-seeking and recovery. Accordingly, there is a pressing need to understand it more clearly, but analyzing the relevant data is highly labor-intensive. Therefore, we designed a chatbot to engage participants in conversations; coded those conversations qualitatively with AI assistance; and, based on those coding results, built causal knowledge graphs to decode stigma. The results we obtained from 1,002 participants demonstrate that conversation with our chatbot can elicit rich information about people's attitudes toward depression, while our AI-assisted coding was strongly consistent with human-expert coding. Our novel approach combining large language models (LLMs) and causal knowledge graphs uncovered patterns in individual responses and illustrated the interrelationships of psychological constructs in the dataset as a whole. The paper also discusses these findings' implications for HCI researchers in developing digital interventions, decomposing human psychological constructs, and fostering inclusive attitudes.

This study used Latent Class Analysis to identify typologies of childhood maltreatment (CM) and the associations of CM with five internalizing symptoms. A sample of 1,042 university students in Singapore answered online self-report questionnaires, inclusive of Childhood Trauma Questionnaire, a modified version of the 14-item Center for Epidemiologic Studies Depression Scale derived from the CES-D, Beck Anxiety Inventory, Post-traumatic Stress Disorder Checklist, Eating Disorder Examination-Questionnaire (version 6.0), and Suicidal Ideation Attributes Scale. These measures respectively assessed CM and current internalizing symptoms, namely, depressive symptoms, anxiety symptoms, PTSD, eating disorder, and suicidal ideation. The most common type of CM was childhood emotional neglect (74.6%), followed by childhood emotional abuse (61%). Men were more likely to experience childhood physical abuse compared to women; contrarily, women were two times more likely to report childhood emotional abuse compared to men. The findings of Latent Class Analysis revealed four distinct latent classes of CM: Low CM, high/multiple CM, moderate to high abuse/victimization, and moderate to high neglect. Students in the latter three CM classes were more likely than those in the Low CM class to report the internalizing symptoms. These findings indicate the importance of protecting children from CM and cushioning the adverse effects of CM on victims by providing timely intervention, both of which would be best achieved with the education of professionals, caregivers and the public alike, and improvements to current programs and practices.

In social service, administrative burdens and decision-making challenges often hinder practitioners from performing effective casework. Generative AI (GenAI) offers significant potential to streamline these tasks, yet exacerbates concerns about overreliance, algorithmic bias, and loss of identity within the profession. We explore these issues through a two-stage participatory design study. We conducted formative co-design workshops (n=27) to create a prototype GenAI tool, followed by contextual inquiry sessions with practitioners (n=24) using the tool with real case data. We reveal opportunities for AI integration in documentation, assessment, and worker supervision, while highlighting risks related to GenAI limitations, skill retention, and client safety. Drawing comparisons with GenAI tools in other fields, we discuss design and usage guidelines for such tools in social service practice.

Smartphone addiction is one of the major social issues among young people these days. The current study aims to identify the determinants of smartphone addiction and examine the association between smartphone addiction and multiple behavioral/psychological problems. The study sample consisted of 1105 university students from Singapore. Students in the high-risk smartphone use group reported higher levels of smartphone addiction, cybervictimization, and depressive and anxiety symptoms. Depressive and anxiety symptoms showed a positive association with smartphone addiction. Additionally, both depressive and anxiety symptoms had significant indirect effects when assessing two separate simple mediation models. When testing the parallel mediation model, the indirect effect of cybervictimization on smartphone addiction occurred through anxiety but not depression. Based on these findings, the current study proposed the implementation of routine screening and the provision of multi-level services in education settings. Nevertheless, the study has limitations related to the study population and the use of self-reported questionnaires.

Cyberbullying victimization and mental health symptoms are major concerns for children and adolescents worldwide. Despite the increasing number of longitudinal studies of cyberbullying and mental health among this demographic, the robustness of the causal associations between cyberbullying victimization and the magnitude of mental health symptoms remains unclear. This meta-analysis investigated the longitudinal impact of cyberbullying victimization on mental health symptoms among children and adolescents. A systematic search identified primary studies published in English between January 2010 and June 2021, yielding a sample of 27 studies encompassing 13,497 children and adolescents aged 8 to 19 years old. The longitudinal association between cyberbullying victimization and mental health symptoms among children and adolescents was found to be weakly positive and consistent across time and age. Three significant moderators were identified: the effect of cyberbullying victimization on mental health was larger among older children, groups with a higher proportion of males, and in more recent publications. No evidence of publication bias was detected. This study adds to the existing body of research by providing a new perspective on the long-term effects of cyberbullying victimization on the mental health of children and adolescents' mental health. Furthermore, it underscores the necessity of developing effective cyberbullying prevention programs, interventions, and legal regulations to comprehensively address this issue.

Cyberbullying and eating disorders are growing public health concerns, particularly among young adults in university settings. The increasing reliance on digital platforms may exacerbate these issues, further impacting mental health. This study examines whether self-esteem mediates the relationship between cybervictimization and eating disorders and whether this mediation is moderated by deviant peer association. Data were collected between August and December 2019 from 723 students (Mean age = 22.8, SD = 2.22) at a public university in Singapore. All participants were smartphone users, with 68.2

Mental-health stigma remains a pervasive social problem that hampers treatment-seeking and recovery. Existing resources for training neural models to finely classify such stigma are limited, relying primarily on social-media or synthetic data without theoretical underpinnings. To remedy this gap, we present an expert-annotated, theory-informed corpus of human-chatbot interviews, comprising 4,141 snippets from 684 participants with documented socio-cultural backgrounds. Our experiments benchmark state-of-the-art neural models and empirically unpack the challenges of stigma detection. This dataset can facilitate research on computationally detecting, neutralizing, and counteracting mental-health stigma. Our corpus is openly available at https://github.com/HanMeng2004/Mental-Health-Stigma-Interview-Corpus.

This study explored the practice experiences of social work practitioners (SWPs) working with increasing family violence cases during the COVID-19 pandemic. Employing a mixed methods approach, the initial quantitative phase involved the administration of the Professional Quality of Life (ProQOL) Scale to 37 SWPs. This was followed by a qualitative phase consisting of two focus group discussions (FGDs) with a total of 10 SWPs. Multivariable analyses using Logistic Regression revealed higher compassion satisfaction was associated with lower burnout and secondary traumatic stress among SWPs. Years of experience and age of SWPs also emerged as significant predictors of their ProQOL. The focus group discussions provided additional insights into the SWPs' practice experiences, elucidating the individual and organisational factors that impacted their ProQOL. Integrating the trauma-informed approach and empowerment model, recommendations of possible interventions to enhance SWPs' ProQOL are also discussed.

Numerous studies have highlighted the profound impacts of violence and discrimination on the LGBTQ+ community, leading to psychosocial stress, isolation, and heightened instances of self-harm. Boal's Theatre of the Oppressed (TO), a community-based intervention, has emerged as a promising approach utilized globally with marginalized groups, including the LGBTQ+ youth population, to foster critical consciousness, amplify marginalized voices, and promote social change. This scoping review aims to explore how TO has been applied as an intervention to address stigma and violence against LGBTQ+ youth. Guided by the PRISMA-ScR framework, seven peer-reviewed articles were identified through a systematic search across SCOPUS, ScienceDirect, and ERIC databases. The review identifies TO's contributions to increasing empathy, encouraging dialogue, and enhancing bystander intervention skills. Despite a noted absence of longitudinal designs or validated measures to assess sustained outcomes, this review emphasizes the potential of TO as a mezzo-level strategy in combatting discrimination faced by LGBTQ+ youth.

Qualitative coding is a demanding yet crucial research method in the field of Human-Computer Interaction (HCI). While recent studies have shown the capability of large language models (LLMs) to perform qualitative coding within theoretical frameworks, their potential for collaborative human-LLM discovery and generation of new insights beyond initial theory remains underexplored. To bridge this gap, we proposed CHALET, a novel approach that harnesses the power of human-LLM partnership to advance theory-driven qualitative analysis by facilitating iterative coding, disagreement analysis, and conceptualization of qualitative data. We demonstrated CHALET's utility by applying it to the qualitative analysis of conversations related to mental-illness stigma, using the attribution model as the theoretical framework. Results highlighted the unique contribution of human-LLM collaboration in uncovering latent themes of stigma across the cognitive, emotional, and behavioral dimensions. We discuss the methodological implications of the human-LLM collaborative approach to theory-based qualitative analysis for the HCI community and beyond.

Although the relationship between bullying victimization and suicidal behaviors of lesbian, gay, bisexual, transgender, and questioning (i.e., unsure of their sexual orientation) students has been well documented in research, few studies have focused on how bullying victimization might be related to suicidal behaviors among youth with intersectional identities. This study examines associations between bullying victimization and suicidal behaviors across racial/ethnic groups in a sample of lesbian, gay, bisexual, and questioning (LGBQ) students. Data for this cross-sectional study were derived from the Center for Disease Control and Prevention's Youth Risk Behavior Survey combined data set (2003–2019), with a sample of 95,603 students who identified as LGBQ. Analyses included descriptive statistics and logistic regression. We found that homophobic bullying victimization was associated with higher odds of suicidal ideation and plans among the total sample and Black and Hispanic students. School-based bullying victimization was associated with higher odds of suicidal ideation, plans, and attempts among white and Hispanic students and higher suicidal ideation among multiracial–non-Hispanic students. Cyberbullying victimization was not associated with suicidal behavior among Asian students, but it was associated with all forms of suicidal behavior among youth of other racial/ethnic identities. Addressing bullying victimization and suicidality with culturally relevant, evidence-based violence prevention strategies is critical.