Introduction: Several studies have demonstrated a directional link between rage rumination and aggression. However, recent research suggests that this relationship is bidirectional. The current study examined the complex relationships between anger rumination and aggression using a moderated network approach in a longitudinal design while considering personal relative deprivation. Method: A total of 665 participants (59.25% female, age mean±SD = 19.01 ± 1.25) were enrolled at two-time points. Assessments included self-report measures of the Anger Rumination Scale, Buss-Perry Aggression Questionnaire, and Relative Deprivation Scale. A Moderated Network Model (MMN) was used to test the complex links among anger rumination, aggression, and personal relative deprivation. Results: The analysis revealed that the link between anger rumination and aggression was complex and bidirectional. Notably, as the level of personal relative deprivation increased, verbal aggression had a positive conditional effect on anger afterthoughts in Wave 2, and thoughts of revenge had a positive conditional effect on verbal aggression in Wave 2. Moreover, as the first discovery, anger afterthoughts and anger had a negative conditional effect on each other across levels of personal relative deprivation in Wave 2. In addition, network comparison indicates that the MNMs structure was significantly different across timepoints, implying that anger rumination and aggression were inextricably linked in college students during isolation and that this complicated relationship was weakened after isolation. Conclusions: This study deepens our understanding of the bidirectional relationships between anger rumination and aggression and recognizes the moderating role of personal relative deprivation.

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Background: The COVID-19 pandemic and the shift to online learning have increased the risk of Internet addiction (IA) among adolescents, especially those who are depressed. This study aims to identify the core symptoms of IA among depressed adolescents using a cross-lagged panel network framework, offering a fresh perspective on understanding the interconnectedness of IA symptoms. Methods: Participants completed the Internet addiction test and the Patient Health Questionnaire-9. A total of 2415 students were initially included, and after matching, only 342 students (a cutoff score of 8) were retained for the final data analysis. A cross-lagged panel network analysis was conducted to examine the autoregressive and cross-lagged trajectories of IA symptoms over time. Results: The incidence rate of depression rose remarkably from 14.16% (N = 342) to 17.64% (N = 426) after the four-month online learning. The symptom of “Anticipation” exhibited the highest out-expected influence within the IA network, followed by “Stay online longer” and “Job performance or productivity suffer”. Regarding the symptom network of depression, “Job performance or productivity suffer” had the highest in-expected influence, followed by “Life boring and empty”, “Snap or act annoyed if bothered”, “Check email/SNS before doing things”, and “School grades suffer”. No significant differences were found in global network strength and network structure between waves 1 and 2. Conclusion: These findings prove the negative effects of online learning on secondary students’ mental health and have important implications for developing more effective interventions and policies to mitigate IA levels among depressed adolescents undergoing online learning.

The long‐term effects of the COVID‐19 pandemic have caused severe mental health problems among college students, which can eventually cause suicidal ideation. Therefore, through network analysis, this study aims to explore the new characteristics of the depression–anxiety symptom network that arose during the long‐term lockdown of the COVID‐19 pandemic and to identify the most influential symptoms linked to suicidal ideation. We used a Patient Health Questionnaire (PHQ‐9) score above 10 as the cutoff and screened 622 participants with an inclination toward depressive disorders from 7976 college students, and then divided the sample into suicidal and nonsuicidal groups based on the presence or absence of suicidal ideation. The General Anxiety Disorder scale (GAD‐7) was also used. Network analysis was used to identify the network structure of anxiety–depression and which symptoms were directly related to suicidal ideation in the network. The prevalence of depression and anxiety among Chinese college students in the late stage of the COVID‐19 pandemic was 7.8% and 17.8%, respectively. The most central symptoms in the nonsuicidal group were “excessive worry,” “uncontrollable worry,” and “nervousness,” and in the suicidal group they were “excessive worry,” “motor function,” and “irritability.” The network of the suicidal group was denser than that of the nonsuicidal group. The most influential symptom directly related to suicidal ideation was “guilt.” The most influential central symptom of depression–anxiety comorbidity characteristics of Chinese adolescents showed a tendency to shift from depression‐oriented (i.e., sad mood) to anxiety‐oriented (i.e., excessive worry) with the continuation of the COVID‐19 pandemic. Treatments or interventions focused on these critical symptoms could be useful in preventing college students from suicide risk.

Loneliness and depression are significant mental health challenges among college students; however, the intricate relationship between these phenomena remains unclear, particularly in the context of self-compassion. In this comprehensive study, we employ a cross-lagged panel network (CLPN) analysis to investigate the symptom-level association between depression and loneliness while exploring the potential moderating influence of self-compassion. Our sample consisted of 2785 college students, who were categorized into high- and low-self-compassion groups based on scores from the Self-Compassion Scale. Depressive symptoms were assessed using the Patient Health Questionnaire-9, while the UCLA Loneliness Scale-8 measured loneliness expressions. Our findings indicate that self-compassion plays a crucial role in the relationship between depression and loneliness. Specifically, we observed distinctive patterns within the high and low-self-compassion groups. In the low-self-compassion group, “energy” emerged as the most influential symptom, whereas “motor function” exhibited the highest influence in the high-self-compassion group. Furthermore, among individuals with high self-compassion, the pathway from depression to loneliness was characterized by “guilt—being alone when desired,” while the reverse path from loneliness to depression encompassed “left out—feeling sad” and “left out—anhedonia.” Conversely, in the low-self-compassion group, depression and loneliness demonstrated a more intricate mutual triggering relationship, suggesting that self-compassion effectively moderates the association between these variables. This study provides valuable insights into the underlying mechanisms driving the interplay between depression and loneliness, shedding light on the pivotal role of self-compassion in this intricate dynamic.