Inhibiting Informal Social Control: How Callous–Unemotional Traits and Body Shame Impede Bystander Intervention in Cyber Delinquency

Abstract

Bystander intervention acts as a critical mechanism of informal social control against cyber delinquency. Grounded in Social Information Processing theory, this study examines the barriers impeding students from acting as capable guardians. Analysis of 423 university students supported a serial mediation model: Callous–unemotional (CU) traits inhibit intervention by eroding perceived social support, subsequently desensitizing individuals to harm severity. Crucially, body shame functioned as a distinct moderator, attenuating the link between harm perception and helping behavior. These findings suggest inaction stems from both emotional detachment and social identity threats. Consequently, prevention efforts should incorporate social support and emotion-regulation strategies to promote bystander intervention in response to cyber delinquency.

Keywords

cyber delinquency bystander intervention callous–unemotional traits body shame informal social control

Introduction

Cyber delinquency, particularly in the form of cyberbullying, has become a significant concern because harmful online interactions can undermine student well-being and weaken prosocial peer norms. The structural features of digital platforms—specifically anonymity and rapid dissemination—can intensify the impact of these behaviors, posing significant challenges for educational administration and justice policy (Kowalski et al., 2014; Oksanen et al., 2020). While formal sanctions are often difficult to enforce in the digital sphere, bystanders play a critical role in informal social control. By intervening, bystanders act as “capable guardians,” shaping the normative environment and halting the victimization process (Salmivalli, 2010). However, a critical gap remains: despite recognizing the severity of these incidents, many potential guardians choose to remain passive. Addressing this gap between bystander awareness and action is therefore a primary objective for effective prevention strategies.

Existing literature offers partial explanations for this passivity. While situational assessments of severity are known to influence intervention willingness (Koehler & Weber, 2018; Macaulay et al., 2019), these judgments are heavily constrained by individual and contextual factors (Faul et al., 2016; Lindegaard et al., 2022). To inform robust intervention policies, research must identify the upstream determinants that compromise a bystander’s capacity to recognize and react to harm. Specifically, callous–unemotional (CU) traits—a personality dimension characterized by low empathy and linked to antisocial propensity—represent a significant risk factor (Haas et al., 2018; Sakai et al., 2012, 2016, 2019, 2023). Conversely, social resources such as perceived social support may function as protective factors that empower individuals to enforce social norms (Q. Li & Hu, 2023; Lin et al., 2025; Liu et al., 2025). Yet, few studies have integrated these risk and protective factors into a unified model to explain the breakdown of guardianship mechanisms.

Furthermore, the translation of risk perception into protective action involves complex emotional regulation. Moral emotions, particularly shame, serve as a double-edged sword in this dynamic. While shame can motivate reparative behaviors to restore social standing, it can also trigger defensive avoidance—especially when individuals fear social exposure or reputation damage (Tangney et al., 2007). In the context of digital victimization, shame may thus moderate the relationship between recognizing harm and taking action, effectively inhibiting the prosocial response required for community safety. Although prior studies have examined CU traits and social support independently (Fang et al., 2022; Y. Zhang et al., 2020), an integrated framework linking personality risks, social resources, and emotional barriers is lacking.

The present study addresses these gaps by testing a moderated serial mediation model among university students. Specifically, we examine whether CU traits inhibit bystander helping behavior—conceptualized here as a key mechanism of informal social control—through a sequential process involving diminished social support and reduced perception of harm severity. Additionally, we investigate whether shame acts as a contextual barrier that moderates the link between harm perception and helping behavior. By delineating these psychological pathways, this study aims to provide empirical evidence for university-based prevention policies, suggesting how targeted interventions can transform passive bystanders into active agents of social control.

Literature Review

Personality Risk and Bystander Helping in Digital Contexts

Personality traits are foundational to social adaptation. Callous–unemotional (CU) traits are widely recognized as a significant risk factor for antisocial behavior and deficits in prosocial engagement (Frick, 2003). For example, individuals high in CU traits often show low empathy, limited guilt or remorse, and reduced concern for others’ distress. Grounded in the social information processing model, high CU individuals exhibit biases in interpreting intentions and recognizing emotional cues, which distort their behavioral responses. Empirical evidence confirms this mechanism: elevated CU traits consistently correlate with aggression, delinquency, and psychological maladjustment across various samples (Essau et al., 2006; Kimonis et al., 2008), predicting negative behavioral outcomes over time (Frick, 2003).

Crucially, CU traits impair emotional processing and social connectivity. Individuals high in CU are less accurate in detecting emotional needs and often overlook cues for help (Paz et al., 2024). Neuroimaging studies reveal reduced activation in empathy-related brain regions (e.g., insula, anterior cingulate cortex) when witnessing distress (Decety et al., 2013; Lockwood et al., 2013). Furthermore, these individuals display lower anticipated guilt, more permissive moral standards, and higher susceptibility to peer rejection (Vasconcelos et al., 2021; Wagner et al., 2020).

These functional deficits undermine interpersonal quality. High CU individuals frequently report lower perceived social support (PSS), partly due to restricted emotional expression (X. Wang et al., 2026). In contrast, PSS serves as a protective factor, fostering emotion regulation and altruism (Luo et al., 2023; Y. Zhang et al., 2024). Therefore, CU traits may indirectly inhibit helping behavior by eroding the quality of social relationships and perceived support.

From Perceived Social Support to Perceived Severity of Harm

Perceived social support (PSS) refers to the extent to which individuals feel emotionally cared for and respected within their social networks (Q. Li & Hu, 2023). Within the social information processing framework, PSS reduces defensive attention, enabling individuals to interpret social cues with greater empathy and responsibility (Crick & Dodge, 1994). Empirical evidence consistently supports this: PSS has been found to mediate prosocial behavior through moral identity, empathic concern, and gratitude (Q. Li & Hu, 2023; Lin et al., 2025; Y. Wang et al., 2024). In digital contexts, high social connectedness and support are strong predictors of bystander intervention (Chen et al., 2025; Y. Huang & Chui, 2024).

PSS is critical for shaping perceived severity of harm—the subjective assessment of potential damage caused by an event (van Noorden et al., 2015). Individuals high in PSS possess greater emotional security, allowing them to detect others’ distress more sensitively and perceive harm as more severe (Zhao et al., 2023). In contrast, those with low PSS often experience emotional blunting, slowing their response to suffering.

According to arousal–cost–reward and empathy–altruism models, elevated perceived severity triggers empathic arousal, thereby motivating helping behavior (Camacho et al., 2018; Roman et al., 2020). Research confirms that among university students, greater perception of harm severity directly predicts stronger intervention intentions (C. L. Huang et al., 2023). Collectively, PSS and perceived severity form a sequential mediation mechanism: CU traits may erode PSS, which in turn reduces cognitive sensitivity to harm (severity), ultimately inhibiting helping behavior.

The Moderating Role of Shame in the Relationship Between Perceived Severity of Harm and Helping Behavior

Shame is a complex self-conscious emotion that plays a dual role in prosocial behavior. Drawing on emotion regulation theories, shame can trigger either defensive avoidance or relational repair depending on situational factors (Tangney et al., 1996).

On one hand, shame often inhibits helping by directing attention inward toward personal flaws. This negative self-evaluation prompts individuals to avoid situations that threaten self-esteem, thereby reducing empathic engagement with others’ distress (Teroni & Bruun, 2011). Unlike guilt, which motivates reparation, shame is closely linked to withdrawal and externalization (Riek et al., 2013; Tangney et al., 2014). Longitudinal evidence confirms that higher shame proneness predicts declines in prosocial orientation (Roos et al., 2014), as individuals may avoid helping to prevent further exposure of their shameful state (Tsumura, 2026).

On the other hand, shame can promote helping when it activates a drive to restore a damaged social image (Leach & Cidam, 2015). Situational publicity is key; when inaction is visible, shame functions as a commitment device, increasing the psychological cost of not helping (Gausel & Leach, 2011). Empirical studies and meta-analyses suggest that shame is more likely to elicit prosocial intentions in high-exposure contexts where reputation is at stake (Guo et al., 2023; S. Li & Wang, 2022).

However, in the specific context of cyberbullying, characterized by high visibility and judgment, the fear of social risks often dominates. If intervention is perceived as a threat to one’s social image, shame is likely to function as an inhibitor. We propose that high levels of shame suppress the helping impulses triggered by harm perception. Thus, shame is hypothesized to moderate the relationship between perceived severity of harm and helping behavior, with higher shame weakening this positive link.

Hypotheses

Based on social information processing theory and research on emotion regulation and moral emotions, the following hypotheses are proposed:

H1. Perceived social support mediates the association between callous–unemotional traits and helping behavior.

H2. Perceived severity of harm mediates the association between callous–unemotional traits and helping behavior.

H3. Perceived social support and perceived severity of harm sequentially mediate the effect of callous–unemotional traits on helping behavior.

H4. Shame moderates the positive association between perceived severity of harm and helping behavior, such that this association is weaker for individuals with higher levels of shame.

Methods

Participants

Participants were 423 undergraduates (150 males, 273 females; M = 19.00, SD = 1.95) recruited via cluster sampling from a university in Sichuan, China. Of 492 distributed questionnaires, 423 provided valid responses, yielding an effective response rate of 86.0%. The study protocol was approved by the institutional review board of the host university, and all participants provided informed consent prior to participation.

Measures

Callous–Unemotional Traits

The Chinese version of the Inventory of Callous–Unemotional Traits (ICU; L. Zhang et al., 2023; original by Frick, 2004) was used to assess personality risk. The scale comprises 24 items across three dimensions: callousness, uncaring, and unemotional. Example items include “I do not care who I might hurt in order to get what I want” and “Other people’s feelings do not matter much to me.” Participants rated items on a 4-point Likert scale ranging from 0 (not at all true) to 3 (definitely true). Internal consistency in the present study was excellent (Cronbach’s α = .91).

Perceived Social Support

Perceived social support was measured using the Chinese version of the Multidimensional Scale of Perceived Social Support (MSPSS; Zimet et al., 1988; Zhou et al., 2005). The scale contains 12 items assessing support from family, friends, and others. An example item is “When I have problems, there are people around me who can help.” Participants indicated agreement on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The scale demonstrated excellent reliability (Cronbach’s α = .97).

Perceived Severity of Harm and Helping Behavior

To assess bystander responses, the research team developed three vignette scenarios depicting common forms of cyberbullying: harassment, exclusion, and outing followed by denigration. In each vignette, a student was portrayed as the target of harmful online behavior by peers. The full vignette texts are provided in Appendix A.

After reading each vignette, participants first reported whether they had previously experienced a similar situation as a victim and whether they had previously engaged in similar behavior as a perpetrator. They then rated each scenario on perceived severity of harm (2 items; e.g., “From the victim’s perspective, how serious is the harm caused by this incident?”) and helping behavior (3 items; e.g., “If you knew the victim, how likely would you be to comfort him or her?”) using a 6-point scale (1 = not at all/unlikely, 6 = very serious/likely). Composite scores were averaged across the three scenarios, with higher scores indicating greater perceived severity of harm and stronger helping behavior. Internal consistency was acceptable for perceived severity (Cronbach’s α = .82) and excellent for helping behavior (Cronbach’s α = .91).

Shame

Shame was assessed using the Chinese version of the Shame Scale (Qian et al., 1999). The scale comprises 29 items covering character, behavioral, body, and family shame. Example items include “Do you feel ashamed of a part of your body or your body shape?” and “Do you feel ashamed when you have done something wrong?” Responses were recorded on a 4-point Likert scale (1 = never, 4 = very often). In the current study, the scale showed excellent internal consistency (Cronbach’s α = .97).

Subjective Socioeconomic Status

Subjective socioeconomic status was assessed using the MacArthur Scale of Subjective Social Status (Adler et al., 2000). Participants viewed a 10-rung ladder representing relative social standing in their community and selected the rung corresponding to their perceived status (1 = lowest standing, 10 = highest standing). Higher positions on the ladder indicated higher perceived levels of wealth, education, and occupational status.

Procedure

Data were collected in classroom settings using paper-and-pencil questionnaires administered by trained undergraduate research assistants. Standardized instructions were provided, and the survey took approximately 20 min to complete. All responses were anonymous to minimize social desirability bias.

Data Analysis

All analyses were conducted using SPSS 26.0. Descriptive statistics and Pearson correlations were computed for primary variables. Tests of normality indicated acceptable distributions, and variance inflation factors (range: 1.01–1.24) confirmed that multicollinearity was not a concern. Hypotheses were tested using Hayes’ (2022) PROCESS macro v4.1. A serial mediation analysis (Model 6) evaluated the path from CU traits to helping behavior via perceived social support and severity of harm. A moderated mediation analysis (Model 87) examined the moderating role of shame. All indirect effects were estimated using bias-corrected bootstrapping with 5,000 resamples and 95% confidence intervals.

Result

Characteristics of Online Behavior Among University Students

Independent sample t-tests were conducted to examine gender differences (see Table 1). Results indicated significant differences across all variables. Males exhibited significantly higher callous-unemotional traits (t = 3.87, p < .01, d = 0.39). In contrast, females scored significantly higher on perceived social support (t = −2.01, p < .05, d = 0.20), perceived severity of harm (t = −3.78, p < .01, d = 0.37), and helping behavior (t = −3.97, p < .01, d = 0.42).

Table 1.

Independent Sample T-Test for Gender Differences.

Parameter	1	2	3	4
Male
M	30.06	57.17	27.83	12.81
SD	7.771	14.81	5.177	3.25
Female
M	27.00	60.19	29.70	14.05
SD	7.791	14.76	4.711	2.65
t	3.872**	−2.010*	−3.780**	−3.970*
Cohen’s d	0.39	0.2	0.37	0.42

Note. 1 = Callous–unemotional traits; 2 = Perceived social support; 3 = Perceived severity of harm; 4 = Helping behavior.

p < .05. **p < .01.

Correlations Among Callous-Unemotional Traits, Perceived Social Support, Perceived Severity of Harm, and Helping Behavior

Pearson correlation analyses were conducted among gender, subjective socioeconomic status (SES), and key study variables (see Table 2). Results generally supported the theoretical hypotheses. Callous–unemotional traits were significantly and negatively correlated with perceived social support (r = −.36, p < .01), perceived severity of harm (r = −.25, p < .01), and helping behavior (r = −.32, p < .01).

Table 2.

Correlations Among All Variables.

Variable	1	2	3	4	5	6	7	8	9	10
1. Gender	—
2. Subjective socioeconomic status	.13**	—
3. Callous–unemotional traits	−.19**	−.10*	—
4. Perceived social support	.10*	.14**	−.36**	—
5. Perceived harm severity	.18**	.10*	−.25**	.32**	—
6. Shame–character	.08	−.08	−.01	−.06	−.03	—
7. Shame–behavior	.02	−.08	−.11*	−.00	.03	.85**	—
8. Shame–body	.07	−.06	.04	−.12*	.02	.77**	.73**	—
9. Shame–family	−.04	−.02	.23**	−.17**	−.04	.57**	.50**	.58**	—
10. Helping behavior	.20**	.12*	−.32**	.34**	.64**	−.13**	−.10*	−.09	−.08	—

p < .05. **p < .01 (two-tailed).

Conversely, perceived social support was positively correlated with both perceived severity of harm (r = .32, p < .01) and helping behavior (r = .34, p < .01). Notably, the strongest association was observed between perceived severity of harm and helping behavior (r = .64, p < .01). Regarding shame dimensions, helping behavior was negatively correlated with character shame (r = −.13, p < .01) and behavioral shame (r = −.10, p < .05), whereas associations with body shame and family shame were not significant. Additionally, being female (r = .20, p < .01) and having higher subjective SES (r = .12, p < .05) were positively linked to Helping behavior.

Relationship Between Callous-Unemotional Traits and Helping Behavior: Test of Chain Mediation Effects

A chain mediation analysis was conducted using PROCESS Model 6 (see Table 3 and Figure 1). The model demonstrated a good fit (R² = .45, F(3, 419) = 114.80, p < .001). Regression results indicated that callous–unemotional traits negatively predicted both perceived social support (b = −0.68, SE = 0.09, t = −7.97, p < .001) and perceived severity of harm (b = −0.10, SE = 0.03, t = −3.25, p = .001). Additionally, perceived social support positively predicted perceived severity of harm (b = 0.09, SE = 0.02, t = 5.28, p < .001). Regarding the outcome variable, helping behavior was positively predicted by both perceived severity of harm (b = 0.34, SE = 0.02, t = 14.87, p < .001) and perceived social support (b = 0.02, SE = 0.01, t = 2.69, p = .007). The direct effect of callous–unemotional traits on helping behavior remained significant and negative (b = −0.05, SE = 0.02, t = −3.51, p = .001).

Table 3.

Results of the Serial Mediation Analysis.

Outcome variable	Predictor variable	b	SE	t	p	95% CL		R ²	F
Outcome variable	Predictor variable	b	SE	t	p	LL	UL	R ²	F
2	1	−0.68	0.09	−7.97	<.001	−0.84	−0.51	.131	63.46
2	Constant	78.15	2.48	31.49	<.001	73.27	83.02
3	1	−0.10	0.03	−3.25	.001	−0.16	−0.04	.122	29.28
	2	0.09	0.02	5.28	<.001	0.05	0.12
	Constant	26.72	1.53	17.46	<.001	23.71	29.73
4	1	−0.05	0.01	−3.51	.001	−0.08	−0.02	.451	114.8
	2	0.02	0.01	2.69	.007	0.01	0.04
	3	0.34	0.02	14.87	<.001	0.3	0.39
	Constant	3.91	0.94	4.15	<.001	2.06	5.76

Note. 1 = Callous–unemotional traits; 2 = Perceived social support; 3 = Perceived severity of harm; 4 = Helping behavior.

Figure 1.

Path diagram of the chain mediation model.

As detailed in Table 4, bias-corrected bootstrapping (5,000 samples) confirmed a significant total indirect effect of callous–unemotional traits on helping behavior (Effect = −0.068, SE = 0.012, 95% CI [−0.092, −0.046]). Specifically, three specific indirect paths were significant: (1) the path mediated by perceived social support (Effect = −0.015, 95% CI [−0.028, −0.003]); (2) the path mediated by perceived severity of harm (Effect = −0.034, 95% CI [−0.055, −0.014]); and (3) the serial mediation path via both perceived social support and perceived severity (Effect = −0.020, SE = 0.007, 95% CI [−0.035, −0.009]). Since no confidence intervals included zero, all mediation effects were statistically significant.

Table 4.

Indirect Effects of Callous–Unemotional Traits on Helping Behavior.

Mediation pathway	Effect	95% confidence interval
Mediation pathway	Effect	LLCI	ULCI
1 → 4 (Direct effect)	−0.051	−0.080	−0.023
1 → 2 → 4	−0.015	−0.028	−0.003
1 → 3 → 4	−0.034	−0.055	−0.014
1 → 2 → 3 → 4	−0.020	−0.035	−0.009

Note. 1 = Callous–unemotional traits; 2 = Perceived social support; 3 = Perceived severity of harm; 4 = Helping behavior.

Test of the Chain Mediation Model Moderated by Shame

The moderated mediation model incorporated body shame as a moderator on the path between perceived severity of harm and helping behavior. The model showed a satisfactory fit (R² = .47, F(5, 417) = 72.44, p < .001), with a significant incremental increase in explanatory power (ΔR² = .01, p < .05; see Table 5 and Figure 2).

Table 5.

Moderated Mediation Model (Model 87) Regression Results.

Predictor	b	SE	t	p	95% CI [LL, UL]
(Constant)	13.956	0.715	19.53	<.001	[12.55, 15.36]
X (Callous–unemotional traits)	−0.051	0.014	−3.56	<.001	[−0.080, −0.023]
M₁ (Perceived social support)	0.019	0.008	2.35	.019	[0.003, 0.034]
M₂ (Perceived harm severity)	0.343	0.023	15.14	<.001	[0.299, 0.388]
W (Body shame)	−0.067	0.032	−2.12	.035	[−0.129, −0.005]
M₂ × W	−0.013	0.006	−2.36	.019	[−0.024, −0.002]

Figure 2.

The moderating role of body shame in the chain mediation model.

Results indicated a significant negative main effect of body shame (b = −0.07, SE = 0.03, t = −2.12, p = .035). Crucially, the interaction term “perceived severity of harm × body shame” was significant (b = −0.01, SE = 0.01, t = −2.36, p = .019). Simple slope analysis (Figure 3) revealed that the positive effect of perceived severity on helping behavior was strongest at low levels of body shame (−1 SD; b = 0.39, p < .001), moderate at mean levels (b = 0.34, p < .001), and weakest at high levels (+1 SD; b = 0.30, p < .001).

Figure 3.

The moderating effect of body shame on the relationship between perceived severity of harm and helping behavior.

Bootstrap analysis confirmed the moderated mediation. The index of moderated mediation was significant (Index = 0.0008, SE = 0.0004, 95% CI [0.0001, 0.0017]). Specifically, the indirect chain effect (CU traits → Social Support → Severity → Helping) was stronger when body shame was low (Effect = −0.0230, 95% CI [−0.0380, −0.0100]) compared to when body shame was high (Effect = −0.0180, 95% CI [−0.0320, −0.0070]).

Supplementary analyses on other shame dimensions (character, behavioral, family) yielded no significant interaction effects (ps > .05), confirming the specificity of body shame as a moderator.

Discussion

Psychological Mechanisms Linking Callous-Unemotional Traits, Social Support, and Perceived Severity of Harm

This study reveals a psychological pathway governing bystander intervention: a progression from emotional detachment to cognitive desensitization, culminating in behavioral withdrawal. Specifically, callous-unemotional (CU) traits not only directly diminish helping behavior but also indirectly inhibit it by eroding perceived social support, which subsequently attenuates the perception of victim distress.

According to Social Information Processing Theory (Crick & Dodge, 1994), CU traits disrupt the sequential steps of social adjustment, particularly cue encoding and interpretation. Neuroimaging evidence suggests that individuals with elevated CU traits exhibit reduced activation in the anterior cingulate cortex and anterior insula when witnessing pain (Lockwood et al., 2013; Michalska et al., 2016). While they may cognitively acknowledge that “harm has occurred,” they lack the corresponding affective resonance (Feilhauer et al., 2013; Frick et al., 2014). This state of “knowing without feeling” predisposes them to indifference. However, social support can serve as a buffer. The perception of being understood and accepted fosters emotional stability and heightens other-oriented attention (Q. Li & Hu, 2023; Luo et al., 2023). It facilitates the reframing of online conflicts as matters requiring intervention, thereby mitigating the diffusion of responsibility (Lin et al., 2025; Y. Wang et al., 2024).

Within this model, perceived severity of harm acts as the pivotal cognitive link. Theories of emotion regulation posit that perceiving an event as severe triggers empathic distress and moral arousal, prompting reparative actions (Fischer et al., 2011; L. Huang et al., 2023). Prior research confirms that subjective appraisals of severity significantly predict willingness to help (Sirgiovanni et al., 2023). In this study, CU traits appear to raise the threshold for perceiving distress, making victims’ situations seem less “severe.” Conversely, as noted by Paleari et al. (2025), social support lowers this threshold, enabling individuals to clearly recognize consequences and engage in compensatory behaviors.

It is crucial to highlight that judging severity in cyber contexts differs from physical reality due to synchronicity and anonymity. While individuals with CU traits typically lack the sensitivity to detect “real pain” from fragmented cues, social support provides an emotional reference frame. In summary, the detrimental effects of CU traits are not immutable. Enhancing perceived social support can reduce the disregard for others’ pain, effectively translating latent emotional resonance into concrete helping behavior.

The Moderating Effect of Body Shame: From Social Exposure to Behavioral Inhibition

This study examined the moderating roles of four shame dimensions, revealing that only body shame significantly inhibited the relationship between perceived severity of harm and helping behavior. Simple slope analyses indicated that while high harm severity promotes helping, this effect is significantly attenuated under conditions of high body shame.

The inhibitory nature of body shame can be understood through its focus on social image. While shame generally arises from perceived failures (Tangney & Dearing, 2002; Tangney et al., 1996), distinct subtypes trigger different responses. Gausel and Leach (2011) posit that shame focused on “self-defects” motivates reparation, whereas shame focused on “social image” triggers avoidance. Body shame falls into the latter category, reflecting a heightened sensitivity to social attractiveness and external scrutiny (Gilbert, 1997). In the high-visibility context of social media (Fox & Vendemia, 2016), individuals with high body shame prioritize self-protection over moral repair. As noted by Dolezal and Lyons (2017), this form of shame employs avoidance to prevent further exposure. Consequently, rather than translating empathy into action, these individuals shift their focus from the victim’s plight to managing their own social status risks (Ferreira et al., 2011), thereby suppressing the impulse to help.

In contrast, character, behavioral, and family shame did not show significant moderating effects. This supports the multidimensionality of shame. Character and behavioral shame are often characterized by self-reflection and guilt (de Hooge et al., 2008, 2018), focusing on “what I did wrong.” These emotions tend to motivate compensatory actions (S. Li & Wang, 2022) rather than inhibition. Family shame, being less immediate to the online context, also failed to interfere. Body shame, however, centers on “how I am seen” (Tangney & Dearing, 2002), making it uniquely disruptive in the public sphere of the internet.

Interpreted through Social Information Processing (SIP) Theory (Crick & Dodge, 1994), high body shame disrupts the “responsibility judgment” stage of information processing. It biases cognitive resources toward social risk assessment (“Will I be judged?”) rather than victim assistance. This aligns with Guo et al.’s (2023) meta-analysis, which asserts that while shame may foster restraint in safe environments, it precipitates defense and avoidance in high-exposure public contexts. Thus, appearance-related anxiety functions as a potent psychological barrier to online prosocial behavior.

Theoretical and Practical Implications and Future Directions

Grounded in Social Information Processing (SIP) Theory, this study expands the understanding of informal social control in digital environments by delineating the interplay between personality risks and emotional barriers. The findings illustrate why potential guardians fail to act: CU traits undermine the detection of victimization cues, acting as a fundamental risk factor for bystander passivity. Conversely, social support serves as a protective resource, facilitating intervention via social referencing. Crucially, the inhibitory role of body shame highlights how self-referential emotions can override the moral imperative to intervene, effectively blocking the enforcement of online social norms. Additionally, disparities related to gender and SES suggest that structural factors indirectly shape the risk assessment frameworks used by students when deciding to uphold justice.

From a policy perspective, effective crime prevention requires addressing both the “capacity” and the “willingness” to intervene. Campus administrators should move beyond general awareness campaigns to implement comprehensive safety strategies. This includes scenario-based training to heighten sensitivity to cyber delinquency and establishing robust, anonymous reporting channels to reduce the social costs of intervention. Importantly, prevention programs must aim to reframe the social meaning of shame—shifting it from a defensive “fear of exposure” to a moral signal that passivity compromises community justice—thereby transitioning its function from inhibitory to reparative (Tangney et al., 2014).

Several limitations warrant consideration. First, the cross-sectional design precludes definitive causal inferences regarding the pathways to intervention; future longitudinal studies are needed to substantiate these causal links. Second, reliance on self-report measures may introduce common method bias. Third, the university sample limits generalizability to broader population sly, as shame is culturally situated, the model requires verification across diverse cultural contexts regarding norms of “public exposure” and collective responsibility.

Future research should advance by: (1) employing longitudinal or experimental designs to capture the dynamic nature of bystander decision-making; (2) integrating objective physiological markers (e.g., Eye-tracking) to provide granular insights into real-time processing of victimization cues; and (3) conducting cross-cultural comparisons to examine how distinct cultural norms modulate the impact of shame on social control behaviors.

Conclusion

In summary, this research elucidates the psychological mechanisms that constrain bystander intervention in cyber delinquency. Callous-unemotional traits fundamentally undermine the recognition of victim suffering, stifling the motivation to enforce social norms at its source. Social support, however, mitigates this deficit by bolstering the capacity for action. While perceived severity of harm acts as the pivotal bridge translating awareness into intervention, shame functions as a contextual barrier, prompting withdrawal due to status anxiety. These findings demonstrate that online intervention is not merely a product of moral judgment, but the outcome of a complex risk assessment involving cognitive appraisal and emotional regulation.

Policy and prevention efforts must therefore address the dual challenges of “perceiving risk” and “daring to act.” Strategies should simultaneously enhance cue detection while fostering safe, supportive campus climates. Such environments facilitate the transformation of shame from a defensive reaction into a constructive catalyst for moral repair. Only through such parallel psychological and structural interventions can bystander behavior transition from silence to collective responsibility, effectively activating the peer group as capable guardians against digital victimization.

Footnotes

Appendix A

ORCID iD

Yilihamu Alimu

Author Contributions

Yilihamu Alimu: Conceptualization, Methodology, Formal analysis, Investigation, Writing—original draft, Writing—review & editing. Tulips Yiwen Wang: Supervision, Critical revisions, Writing—review & editing, Correspondence handling. Junyang Chen: Project administration, Data collection, Literature assistance. Jiajun Jiang: Literature search, Reference checking, Writing—review & editing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.*

Author Biographies

Yilihamu Alimu is a PhD student in the School of Education, Queensland University of Technology. His research examines bullying and cyberbullying from social-psychological and behavioural perspectives, with a particular focus on bystander behaviour, peer influence, and the social mechanisms that sustain peer aggression.

Dr. Tulips Yiwen Wang is an associate professor in the Institute of Analytical Psychology, Faculty of Health and Wellness, City University of Macau. Her research centers on social psychology, with a focus on interpersonal relationships and interactions, and their implications for mental health.

Junyang Chen is a psychotherapist in the Department of Clinical Psychology at Xiamen Xian Yue Hospital and a postgraduate student at City University of Macau. His research focuses on bullying and cyberbullying. He primarily examines the social mechanisms and influencing factors that sustain peer bullying behavior.

Jiajun Jiang is a postgraduate student at the Institute of Analytical Psychology, Faculty of Health and Wellness, City University of Macau. His research focuses on social psychology.

References

Adler

N. E.

Epel

E. S.

Castellazzo

Ickovics

J. R.

(2000). Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy, White women. Health Psychology, 19(6), 586–592. https://doi.org/10.1037/0278-6133.19.6.586

Camacho

Hassanein

Head

(2018). Cyberbullying impacts on victims’ satisfaction with information and communication technologies: The role of perceived cyberbullying severity. Information & Management, 55(4), 494–507. https://doi.org/10.1016/j.im.2017.11.004

Chen

Sun

I. Y.

Wang

(2025). Social bonds, sense of powerlessness and intervention actions in cyberbullying among Chinese college students. Journal of Criminology. Advance online publication. https://doi.org/10.1177/26338076251320125

Crick

N. R.

Dodge

K. A.

(1994). A review and reformulation of social information-processing mechanisms in children’s social adjustment. Psychological Bulletin, 115(1), 74–101. https://doi.org/10.1037/0033-2909.115.1.74

de Hooge

I. E.

Breugelmans

S. M.

Wagemans

F. M. A.

Zeelenberg

(2018). The social side of shame: Approach versus withdrawal. Cognition and Emotion, 32(8), 1671–1677. https://doi.org/10.1080/02699931.2017.1422696

de Hooge

I. E.

Breugelmans

S. M.

Zeelenberg

(2008). Not so ugly after all: When shame acts as a commitment device. Journal of Personality and Social Psychology, 95(4), 933–943. https://doi.org/10.1037/a0011991

Decety

Skelly

L. R.

Kiehl

K. A.

(2013). Brain response to empathy-eliciting scenarios involving pain in incarcerated individuals with psychopathy. JAMA Psychiatry, 70(6), 638–645. https://doi.org/10.1001/jamapsychiatry.2013.27

Dolezal

Lyons

(2017). Health-related shame: An affective determinant of health? Medical Humanities, 43(4), 257–263. https://doi.org/10.1136/medhum-2017-011186

Essau

C. A.

Sasagawa

Frick

P. J.

(2006). Callous-unemotional traits in a community sample of adolescents. Assessment, 13(4), 454–469. https://doi.org/10.1177/1073191106287354

10.

Fang

Wang

Gao

Yang

Wang

Wen

(2022). Childhood maltreatment and adolescent cyberbullying perpetration: A moderated mediation model of callous—unemotional traits and perceived social support. Journal of Interpersonal Violence, 37(7–8), NP5026–NP5049. https://doi.org/10.1177/0886260520960106

11.

Faul

Aikman

S. N.

Sasser

S. M.

(2016). Bystander intervention prior to the arrival of emergency medical services: Comparing assistance across types of medical emergencies. Prehospital Emergency Care, 20(3), 317–323. https://doi.org/10.3109/10903127.2015.1088605

12.

Feilhauer

Cima

Benjamins

Muris

(2013). Knowing right from wrong, but just not always feeling it: Relations among callous-unemotional traits, psychopathological symptoms, and cognitive and affective morality judgments in 8- to 12-year-old boys. Child Psychiatry & Human Development, 44(6), 709–716. https://doi.org/10.1007/s10578-013-0363-0

13.

Ferreira

Pinto-Gouveia

Duarte

(2011). Physical appearance as a measure of social ranking: The role of a new scale to understand the relationship between weight and dieting. Clinical Psychology & Psychotherapy, 20(1), 55–66. https://doi.org/10.1002/cpp.769

14.

Fischer

Krueger

J. I.

Greitemeyer

Vogrincic

Kastenmüller

Frey

Heene

Wicher

Kainbacher

(2011). The bystander-effect: A meta-analytic review on bystander intervention in dangerous and non-dangerous emergencies. Psychological Bulletin, 137(4), 517–537. https://doi.org/10.1037/a0023304

15.

Fox

Vendemia

M. A.

(2016). Selective self-presentation and social comparison through photographs on social networking sites. Cyberpsychology, Behavior, and Social Networking, 19(10), 593–600. https://doi.org/10.1089/cyber.2016.0248

16.

Frick

P. J.

(2003). Callous-unemotional traits and conduct problems in the prediction of conduct problem severity, aggression, and self-report of delinquency. Journal of Abnormal Child Psychology, 31(4), 457–470. https://doi.org/10.1023/a:1023899703866

17.

Frick

P. J.

(2004). Inventory of Callous–Unemotional Traits. APA PsycTests. https://doi.org/10.1037/t62639-000

18.

Frick

P. J.

Ray

J. V.

Thornton

L. C.

Kahn

R. E.

(2014). Annual research review: A developmental psychopathology approach to understanding callous-unemotional traits in children and adolescents with serious conduct problems. Journal of Child Psychology and Psychiatry, 55(6), 532–548. https://doi.org/10.1111/jcpp.12152

19.

Gausel

Leach

C. W.

(2011). Concern for self-image and social image in the management of moral failure: Rethinking shame. European Journal of Social Psychology, 41(4), 468–478. https://doi.org/10.1002/ejsp.803

20.

Gilbert

(1997). The evolution of social attractiveness and its role in shame, humiliation, guilt and therapy. British Journal of Medical Psychology, 70(2), 113–147. https://doi.org/10.1111/j.2044-8341.1997.tb01893.x

21.

Guo

Tian

Bai

Zhou

(2023). The effects of shame on prosocial behavior: A systematic review and three-level meta-analysis. Advances in Psychological Science, 31(3), 371–385. https://doi.org/10.3724/sp.j.1042.2023.00371

22.

Haas

S. M.

Becker

S. P.

Epstein

J. N.

Frick

P. J.

(2018). Callous–unemotional traits are uniquely associated with poorer peer functioning in school-aged children. Journal of Abnormal Child Psychology, 46(4), 781–793. https://doi.org/10.1007/s10802-017-0330-5

23.

Hayes

A. F.

(2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.

24.

Huang

C. L.

Alimu

Yang

S. C.

(2023). Factors influencing ethnic minority students’ helping behavior in cyberbullying: Perceived severity of cyberbullying from various perspectives, the online disinhibition effect, and parental online discipline style. European Journal of Psychology of Education, 39, 1889–1911. https://doi.org/10.1007/s10212-023-00738-w

25.

Huang

Sun

Wang

Zhou

(2023). The severity of cyberbullying affects bystander intervention among college students: The roles of feelings of responsibility and empathy. Psychology Research and Behavior Management, 16, 893–903. https://doi.org/10.2147/prbm.s397770

26.

Huang

Chui

(2024). Bullying victims’ perceived social support and psychological health and prosocial behavior: A latent profile analysis. Journal of Youth and Adolescence, 53, 1683–1698. https://doi.org/10.1007/s10964-024-01954-3

27.

Kimonis

E. R.

Frick

P. J.

Skeem

J. L.

Marsee

M. A.

Cruise

Munoz

L. C.

Aucoin

K. J.

Morris

A. S.

(2008). Assessing callous–unemotional traits in adolescent offenders: Validation of the Inventory of Callous–Unemotional Traits. International Journal of Law and Psychiatry, 31(3), 241–252. https://doi.org/10.1016/j.ijlp.2008.04.002

28.

Koehler

Weber

(2018). “Do I really need to help?!” Perceived severity of cyberbullying, victim blaming, and bystanders’ willingness to help the victim. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 12(4), Article 4. https://doi.org/10.5817/cp2018-4-4

29.

Kowalski

R. M.

Giumetti

G. W.

Schroeder

A. N.

Lattanner

M. R.

(2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140(4), 1073–1137. https://pubmed.ncbi.nlm.nih.gov/24512111/

30.

Leach

C. W.

Cidam

(2015). When is shame linked to constructive approach orientation? A meta-analysis. Journal of Personality and Social Psychology, 109(6), 983–1002. https://doi.org/10.1037/pspa0000037

31.

(2023). Positive impacts of perceived social support on prosocial behavior: The chain mediating role of moral identity and moral sensitivity. Frontiers in Psychology, 14, Article 1234977. https://doi.org/10.3389/fpsyg.2023.1234977

32.

Wang

(2022). The effect of shame on prosocial behavior tendency toward a stranger. BMC Psychology, 10(1), Article 308. https://doi.org/10.1186/s40359-022-01021-1

33.

Lin

Wang

T. Y.

Liu

(2025). From perceived social support to prosocial behavior: The serial mediating role of sense of gain and gratitude. BMC Psychology, 13(1), Article 894. https://doi.org/10.1186/s40359-025-03082-4

34.

Lindegaard

M. R.

Liebst

L. S.

Philpot

Levine

Bernasco

(2022). Does danger level affect bystander intervention in real-life conflicts? Evidence from CCTV footage. Social Psychological and Personality Science, 13(4), 795–802. https://doi.org/10.1177/19485506211042683

35.

Liu

Zhuang

Cao

(2025). The impact of negative life events on psychological capital, perceived social support and prosocial behavior in medical students: A moderated mediation model. BMC Medical Education, 25(1), Article 811. https://doi.org/10.1186/s12909-025-07410-5

36.

Lockwood

P. L.

Sebastian

C. L.

McCrory

E. J.

Hyde

Z. H.

De Brito

S. A.

Viding

(2013). Association of callous traits with reduced neural responses to others’ pain in children with conduct problems. Current Biology, 23(10), 901–905. https://doi.org/10.1016/j.cub.2013.04.018

37.

Luo

Deng

(2023). School climate and adolescents’ prosocial behavior: The mediating role of perceived social support and resilience. Frontiers in Psychology, 14, Article 1095566. https://doi.org/10.3389/fpsyg.2023.1095566

38.

Macaulay

P. J. R.

Boulton

M. J.

Betts

L. R.

(2019). Comparing early adolescents’ positive bystander responses to cyberbullying and traditional bullying: The impact of severity and gender. Journal of Technology in Behavioral Science, 4(3), 253–261. https://doi.org/10.1007/s41347-018-0082-2

39.

Michalska

K. J.

Zeffiro

T. A.

Decety

(2016). Brain response to viewing others being harmed in children with conduct disorder symptoms. Journal of Child Psychology and Psychiatry, 57(4), 510–519. https://doi.org/10.1111/jcpp.12474

40.

Oksanen

Oksa

Savela

Kaakinen

Ellonen

(2020). Cyberbullying victimization at work: Social media identity bubble approach. Computers in Human Behavior, 109, Article 106363. https://doi.org/10.1016/j.chb.2020.106363

41.

Paleari

F. G.

Cavagnis

Ertan

Fincham

F. D.

(2025). Moral transgressions, psychological well-being, and family conflict in the context of the COVID-19 pandemic: The role of self-forgiveness. BMC Psychology, 13(1), Article 300. https://doi.org/10.1186/s40359-025-02513-6

42.

Paz

All

Kohli

Plate

R. C.

Viding

Waller

(2024). Why should I? Examining how childhood callous-unemotional traits relate to prosocial and affiliative behaviors and motivations. Research on Child and Adolescent Psychopathology, 52(7), 1075–1087. https://doi.org/10.1007/s10802-024-01170-4

43.

Qian

Huang

Xiao

(1999). Study on the correlation of shame with parenting style, self-esteem, achievement motivation, and locus of control. Chinese Journal of Clinical Psychology, 7(3), 21–23.

44.

Riek

B. M.

Luna

L. M. R.

Schnabelrauch

C. A.

(2013). Transgressors’ guilt and shame. Journal of Social and Personal Relationships, 31(6), 751–772. https://doi.org/10.1177/0265407513503595

45.

Roman

Rad

Egerau

Dixon

Dughi

Kelemen

Balas

Rad

(2020). Physical self-schema acceptance and perceived severity of online aggressiveness in cyberbullying incidents. Journal of Interdisciplinary Studies in Education, 9(1), 100–116. https://doi.org/10.32674/jise.v9i1.1961

46.

Roos

Hodges

E. V. E.

Salmivalli

(2014). Do guilt- and shame-proneness differentially predict prosocial, aggressive, and withdrawn behaviors during early adolescence? Developmental Psychology, 50(3), 941–946. https://doi.org/10.1037/a0033904

47.

Sakai

J. T.

Chintaluru

Raymond

K. M.

McWilliams

McKell Carter

Winters

D. E.

Mikulich-Gilbertson

S. K.

(2023). A revised prosocial behavior game: Testing associations with psychopathic traits and the effects of moral elevation using a randomized clinical trial. PLOS One, 18(4), Article e0283279. https://doi.org/10.1371/journal.pone.0283279

48.

Sakai

J. T.

Dalwani

M. S.

Gelhorn

H. L.

Mikulich-Gilbertson

S. K.

Crowley

T. J.

(2012). A behavioral test of accepting benefits that cost others: Associations with conduct problems and callous-unemotionality. PLOS ONE, 7(4), Article e36158. https://doi.org/10.1371/journal.pone.0036158

49.

Sakai

J. T.

Dalwani

M. S.

Mikulich-Gilbertson

S. K.

McWilliams

S. K.

Raymond

K. M.

Crowley

T. J.

(2016). A behavioral measure of costly helping: Replicating and extending the association with callous unemotional traits in male adolescents. PLOS ONE, 11(3), Article e0151678. https://doi.org/10.1371/journal.pone.0151678

50.

Sakai

J. T.

Raymond

K. M.

McWilliams

S. K.

Mikulich-Gilbertson

S. K.

(2019). Testing helping behavior and its relationship to antisocial personality and psychopathic traits. Psychiatry Research, 274, 98–104. https://doi.org/10.1016/j.psychres.2019.02.022

51.

Salmivalli

(2010). Bullying and the peer group: A review. Aggression and Violent Behavior, 15(2), 112–120. https://doi.org/10.1016/j.avb.2009.08.007

52.

Sirgiovanni

Smolenski

Abelson

Webb

(2023). The moralizing effect: Self-directed emotions and their impact on culpability attributions. Frontiers in Integrative Neuroscience, 17, Article 1232523. https://doi.org/10.3389/fnint.2023.1232523

53.

Tangney

J. P.

Dearing

R. L.

(2002). Shame and guilt. Guilford Press. https://doi.org/10.4135/9781412950664.n388

54.

Tangney

J. P.

Miller

R. S.

Flicker

Barlow

D. H.

(1996). Are shame, guilt, and embarrassment distinct emotions? Journal of Personality and Social Psychology, 70(6), 1256–1269. https://doi.org/10.1037/0022-3514.70.6.1256

55.

Tangney

J. P.

Stuewig

Martinez

A. G.

(2014). Two faces of shame: The roles of shame and guilt in predicting recidivism. Psychological Science, 25(3), 799–805. https://doi.org/10.1177/0956797613508790

56.

Tangney

J. P.

Stuewig

Mashek

D. J.

(2007). Moral emotions and moral behavior. Annual Review of Psychology, 58(1), 345–372. https://doi.org/10.1146/annurev.psych.56.091103.070145

57.

Teroni

Bruun

(2011). Shame, guilt and morality. Journal of Moral Philosophy, 8(2), 223–245. https://doi.org/10.1163/174552411x563574

58.

Tsumura

(2026). Mediating effects of guilt and shame on the helping behavior of people who have witnessed ostracism. Psychological Reports, 129(1), 405–414. https://doi.org/10.1177/00332941241239009

59.

van Noorden

T. H. J.

Bukowski

W. M.

Haselager

G. J. T.

Lansu

T. A. M.

Cillessen

A. H. N.

(2015). Disentangling the frequency and severity of bullying and victimization in the association with empathy. Social Development, 25(1), 176–192. https://doi.org/10.1111/sode.12133

60.

Vasconcelos

Viding

Sebastian

C. L.

Faria

Almeida

P. R.

Gonçalves

Ó. F.

Gonçalves

R. A.

Sampaio

Seara-Cardoso

(2021). Callous-unemotional traits moderate anticipated guilt and wrongness judgments to everyday moral transgressions in adolescents. Frontiers in Psychiatry, 12, Article 625328. https://doi.org/10.3389/fpsyt.2021.625328

61.

Wagner

N. J.

Bowker

J. C.

Rubin

K. H.

(2020). Associations between callous-unemotional traits and peer-rated social-behavioral outcomes in elementary and middle school. Journal of Abnormal Child Psychology, 48, 757–769. https://doi.org/10.1007/s10802-020-00636-5

62.

Wang

Wei

Wang

(2026). Adolescents high in callous-unemotional traits are prone to be bystanders: The roles of moral disengagement, moral identity, and perceived social support. Child Psychiatry & Human Development, 57(1), 241–253. https://doi.org/10.1007/s10578-024-01709-y

63.

Wang

Zhang

Sun

Zhang

(2024). Perceived social support and prosocial behavior in medical students: Mediating effect of empathy and moderating role of moral identity. Acta Psychologica, 250, Article 104543. https://doi.org/10.1016/j.actpsy.2024.104543

64.

Zhang

Chen

Hong

Zhao

Fan

Liu

(2023). The relationship between callous-unemotional traits and school bullying of junior high school students: A moderated mediation model. Psychological Development and Education, 39(2), 266–275. https://doi.org/10.16187/j.cnki.issn1001-4918.2023.02.13

65.

Zhang

Han

Zhang

Wang

(2020). The influence of campus exclusion on the behavior of externalization problems of junior middle school students: A mediating effect with regulation. Studies of Psychology and Behavior, 18(3), 354–360.

66.

Zhang

Cui

Wang

(2024). Longitudinal relationships between perceived social support and social behaviors in preadolescence and early adolescence: The mediating role of social self-concept. Current Psychology, 43, 28292–28305. https://doi.org/10.1007/s12144-024-06448-w

67.

Zhao

Chu

Rong

(2023). Cyberbullying experience and bystander behavior in cyberbullying incidents: The serial mediating roles of perceived incident severity and empathy. Computers in Human Behavior, 138, Article 107484. https://doi.org/10.1016/j.chb.2022.107484

68.

Zhou

Qian

Jiang

Wang

(2005). Comparison of coping styles and social support among different occupational groups and their relationship. Chinese Journal of Health Psychology, 13(6), 424–426.

69.

Zimet

G. D.

Dahlem

N. W.

Zimet

S. G.

Farley

G. K.

(1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 52(1), 30–41. https://doi.org/10.1207/s15327752jpa5201_2