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Adolescents' online anger and aggressive behavior: Moderating effect of seeking social support.

Individuals spread anger faster and more widely online than they do in real life, and they are more likely to lack emotional control and exhibit irrational behaviors (Turel & Qahri-Saremi, 2018). Many adolescents impulsively engage in aggressive behavior when their anger is aroused in daily life (Wilkowski & Robinson, 2008), especially online where individuals have almost total freedom of speech and their actions are concealed by the nature of the Web. Therefore, researchers have been studying the prevalence and social effects of online anger in adolescents (e.g., Novin, Bos, Stevenson, & Rieffe, 2018; Thornberg & Jungert, 2014). Adolescents are predisposed to engage in aggressive behavior when they experience anger because they tend to be more sensitive and have greater emotional instability than do adults (Niedenthal & Brauer, 2012). It may be that emotional information spreads faster in cyberspace than in the real world, and that emotions become amplified, because bystanders tend to be complacent and do not give the desired response, leading to individuals who are experiencing anger easily becoming aggressive. It should be noted, however, that the relationship between anger and online aggression can be bidirectional. That is, aggressive individuals may also be more likely to become angry due to a multitude of factors, such as hostile attribution and environmental risks (Orobio De Castro, Veerman, Koops, Bosch, & Monshouwer, 2002). Anger refers to a tendency to be irritable; it is not simply an emotional state, but a personality trait by which individuals evaluate emotional situations in an angry way (Ramirez & Andreu, 2006). Researchers have shown that anger is related to increased interpersonal aggression (Srivastava & Singh, 2015). On one hand, emotional information associated with anger biases a person toward engaging in aggressive behavior; that is, people who prefer to deal with social information in an angry way are more likely to exhibit aggression (Bandura, 1977). On the other hand, adolescents lack the ability to regulate anger consistently because they are still undergoing psychosocial and physiological development (Silk, Steinberg, & Morris, 2003). Nonetheless, young people can use appropriate strategies to regulate their interpersonal emotional expressions and to manage their anger. For example, seeking social support and appropriately expressing one's feelings are effective strategies recommended by health psychologists (Gross, 2001).

Cyber aggression among adolescents has emerged as a significant problem that can interfere with adolescents' social development (Wegge, Vandebosch, Eggermont, & Walrave, 2014). Grigg (2010) defined cyber aggression as any behavior that psychologically injures another person or other persons, and that the victim(s) desire(s) to prevent from happening, which is enacted through the application of computer technology. Researchers have acknowledged that cyber aggression is an important form of dysfunctional behavior at the societal level; it includes sending aggressive messages and rejecting, excluding, ignoring, and denying requests for harmony (Wright et al., 2015). There have been many studies on cyber aggression from the perspective of proactive aggression but few on reactive aggression. Reactive aggression is a defensive response to provocation or trouble that is used by victims (Salmivalli & Nieminen, 2002). Based on self-control theory, when victims perceive loss of control over their surroundings, they experience psychological problems, such as social withdrawal, emotional disorders, and low life satisfaction. Individuals who experience negative emotions are prone to make inadequate behavioral responses, such as aggression (Baumeister, Heatherton, & Tice, 1994). Therefore, when victims have incremental negative experiences, they should endeavor to exert control over their social circumstances and decrease their feelings of aggression through emotion regulation strategies (Muraven & Baumeister, 2000).

Social support is an important factor in reducing anger and improving social adaptability (Thompson, 2015). Social support is the state of being loved and cared for (Wills, 1991) and includes information support (e.g., coping strategies and referral sources), instrumental support (e.g., offering material help, such as financial support, services, and other aid or goods), and emotional support (e.g., providing emotional warmth, trust, and empathy). Seeking social support is a positive strategy for dealing with difficulties and is an important factor in determining how a person responds to stressful events, such as online interpersonal provocations, social disapproval, and social discrimination (Sampson, DeArmond, & Chen, 2014). However, in some circumstances perceived social support is a better predictor of adjustment to events that provoke anger than is seeking support (Lu & Hampton, 2016). Scarpa and Haden (2006) found that as community violence victimization increased, perceived social support mitigated aggressive behavior. Seeking support may even be a contributing cause of emotions such as anger, anxiety, depression, distress, and a sense of incompetence, because it requires emotional and cognitional cost (Bolger, Zuckerman, & Kessler, 2000; Taylor, 2011). The main reason for these inconsistent findings may be different definitions and measurements of seeking support, and future researchers should take into account the difference between the amount of social support provided and the amount sought. It is only when individuals receive less social support than they provide to others that they tend to seek social support for themselves (Carpenter, 2012). Hence, our aim with the present study was to investigate whether the direct pathway between the online anger of adolescents and cyber aggression would be moderated by seeking social support. Despite a premise held in developmental psychology studies that seeking social support can have an effect on aggressive behavior in the virtual world (Hunter, Boyle, & Warden, 2004; Scarpa & Haden, 2006), few researchers have tested this premise (Vollink, Bolman, Dehue, & Jacobs, 2013). Based on the research discussed above, we formed the following competing hypotheses:

Hypothesis 1: Online social support will decrease the intensity of adolescents' cyber aggression.

Hypothesis 2: Online social support will increase the intensity of adolescents' cyber aggression.

In this cross-sectional study, we formed the following further hypotheses:

Hypothesis 3: Individuals who feel greater anger will report more aggressive behavior in social networking environments.

Hypothesis 4: Online social support will better attenuate experiences of cyber aggression in the context of mild online anger.


Participants and Procedure

Participants were recruited by means of the cluster sampling method from three high schools in the western regions of Anhui Province, China. They were 509 adolescents (287 young women and 222 young men; [M.sub.age] = 16.41 years, SD = 3.32). We divided participants into three groups based on their daily pattern of Internet use during the past 3 months: (1) more than six hours per day (n = 153); (2) between 3 and 6 hours per day (n = 225); and (3) fewer than three hours per day (n = 131). Preliminary analyses showed there was no significant difference between the groups on any of the variables we investigated. Each participant who was 18 years of age or older signed an informed consent form, and the guardians of those who were under 18 years of age granted consent for their children to take part in the study. The participants were informed that they could leave the study at any time. This consent procedure and the study itself were approved by the Research Ethics Committee of West Anhui University.


Adolescent Online Reactive Aggressive Behavior Scale. This scale, which is based on Zhao and Gao's (2012) Adolescent Cyber Aggression Scale, is used to assess the degree to which individuals engage in online reactive aggression. The reliability and validity of the scale have been reported in a previous study (Zhao & Gao, 2012). Participants are asked to indicate how often they engaged in reactive aggression during the past month when they felt provoked to anger by others on the Internet. The scale comprises five items that are rated from 1 (never) to 5 (all the time). A sample item is "Exclude someone from our network of friends." Cronbach's alpha was .85 in the present study.

Online Anger Scale. This scale (Dillard & Shen, 2005) involves instructing participants to imagine a scenario where a "netizen" declines to comment on a participant's status updates on the Internet as much as the participant would have liked, after which they are asked to rate each of four emotions (irritated, angry, annoyed, and aggravated) using a 7-point scale ranging from 1 = I did not feel this emotion to 7 = I strongly felt this emotion. This scale has been used widely in ethnically diverse populations and has been shown to have high reliability and validity (Dillard & Shen, 2005). Cronbach's alpha was .85 in the present study.

Online Social Support Seeking Scale. This scale investigates the difference between the amount of social support participants provide and the amount they seek online using a 7-point Likert scale ranging from 1 = strongly disagree to 7 = strongly agree (Carpenter, 2012). The scale is composed of two subscales: a four-item subscale that measures the amount of social support individuals provided and a four-item subscale that measures the amount of social support they sought. The score for seeking social

support online was calculated by subtracting the amount they sought from the amount they provided. We conducted confirmatory factor analysis to test the construct validity of the scales in a Chinese context. Results showed the scale had a good fit ([chi square]/df = 3.03, goodness-of-fit index = .91, comparative fit index = 0.85, and root mean square error of approximation = .06). Cronbach's alpha was .86 in this study.

Data Analysis

Independent-samples t tests, multiple regression, calculation of reliability coefficients, and the Johnson--Neyman test were performed using SPSS version 20.0 software. Interaction effects were plotted following Hayes' (2013) procedures.


The relationships among cyber aggression (M = 1.67 [+ or -] 0.80), seeking online social support (M = 0.57 [+ or -] 1.43), and online anger (M = 2.11 [+ or -] 1.50) were investigated using Pearson's product-moment correlation coefficient. The correlation between cyber aggression and seeking online social support was negative (r = -.127, p < .05) and the correlation between cyber aggression and online anger was positive (r = .377, p < .01). The results indicate that higher levels of seeking online social support were associated with lower levels of cyber aggression; however, higher levels of online anger were associated with higher levels of cyber aggression.

We conducted multiple regression to test the moderating effect of seeking online social support on the relationship between online anger and cyber aggression. Cyber aggression data were nearly normally distributed because they showed a moderate positive skew of 0.16. The interaction term for seeking online social support x online anger was entered into the regression model after the main effects of seeking online social support and online anger were assessed (Cohen, West, & Aiken, 1975). Table 1 shows that higher levels of seeking social support online did not significantly predict cyber aggression. However, the main effects of online anger and the interaction of seeking online social support with online anger were significant. We plotted cyber aggression against online anger, separately, for high and low levels of seeking online social support (one standard deviation below the mean and one standard deviation above the mean). Figure 1 shows that the slope of the line for low social-support seeking was steeper than the slope for high social-support seeking, clearly illustrating the moderating effect of seeking online social support. For adolescents with low social-support seeking, higher anger was associated with higher cyber aggression. However, for adolescents with high social-support seeking, the effect of online anger was not significantly associated with cyber aggression; these youths had similar levels of cyber aggression whether their level of online anger was low or high.

The Johnson--Neyman test was used to explore the interaction effect further. The results show that the association between online anger and cyber aggression was not significant among adolescents who reported high levels of seeking social support. Specifically, the simple slope coefficient for anger was not significant ([gamma] = 0.088, p = .05) for individuals who had a social-support-seeking score of 3.09 or above.


We found that there was a positive relationship between cyber aggression and online anger. However, these two variables were negatively correlated with seeking online social support. As expected, seeking online social support had a negative association with online aggression; that is, seeking social support online appeared to inhibit online aggression to a certain extent. The moderation analysis showed, as hypothesized, that the effect of online anger on cyber aggression was moderated by seeking social support online.

Aggression is related to mental health problems in adolescents, including the internalization of problems through social anxiety, depression, and other negative emotions (American Psychiatric Association, 2013). Previous researchers have shown that hostile emotional experiences, especially anger, produce reactive aggression, which is likely to occur as temporary displays of anger that are mainly emotionally driven rather than derived from social cognition of, for example, hostile attribution (Hubbard, Dodge, Cillessen, Coie, & Schwartz, 2001). According to the conceptualization of intentions and conditions, the aggression investigated in the present study is commonly viewed as reactive (Salmivalli & Nieminen, 2002). Moreover, as adolescents age, being ignored or put down, and other threats to self-esteem, become more important causes of aggression than other factors such as demographic and personality features (Cale & Lilienfeld, 2006). Therefore, our results suggest that online anger produced online aggressive behavior.

Our findings contrast with those in some studies in which seeking online social support reinforces active cyber aggression (Renati, Berrone, & Zanetti, 2012; Runions & Bak, 2015). Our view is that these differences are more apparent than real and hinge on the choice of the reference point. Some researchers have found that seeking online social support, a type of response-focused emotional regulation strategy in line with emotion regulation theory (Gross, 2001), increases cyber aggression when anger has been induced (Sukhodolsky, Golub, & Cromwell, 2001; Turner & White, 2015). In addition, seeking social support has been found to lead individuals to underestimate their own aggressive behavior (Farnicka & Grzegorzewska, 2015). However, other researchers have examined how seeking social support weakens aggression (see, e.g., Scarpa & Haden, 2006), and found that, compared to a circumstance in which there is no social support, having social support decreases hostility, and seems to be more effective in dampening negative emotions after an intense episode of anger (Hubbard et al., 2001). In previous empirical studies on social support and aggression, the focus has tended to be on perceived social support and has ignored the received support (Holt & Espelage, 2007; Scarpa & Haden, 2006). Therefore, our aim in this study was to explore the effect on aggression of seeking social support as a propensity for social support. Regardless of the direction of effect, therefore, our findings suggest that angry adolescents are less aggressive when they seek more social support in the online network context. Reactive aggression, a defensive response to trouble, is associated with interpersonal conflict, and, in turn, is salient to social support (Farnicka & Grzegorzewska, 2015). In the current study, it appears that individuals with abundant online social support resources were able to quickly seek help from others and were less likely to display aggressive behavior when they felt anger induced by external circumstances.

Although our findings show a moderating effect of seeking online social support on the relationship between adolescents' online anger and cyber aggression behavior on the Internet, we did not investigate other emotions, which may be considered a limitation. Hence, future researchers might ask whether our results are applicable to other emotions that may occur in online situations. Only one specific scenario, that is, when participants felt provoked to anger by others on the Internet, was used in the current study, and it activated anger to the extent we had anticipated. However, prior research findings show that cyber aggression is positively correlated with several emotions, including loneliness, depression, and distress, as well as with anger (Trnka, Martinkova, & Tavel, 2015). It would be worthwhile for future researchers to use different online scenarios designed to activate different kinds of emotions, and investigate key negative emotions that may produce cyber aggression. A second limitation of our study is that a self-report questionnaire was the only one we used to measure emotion, when in fact, the automatic processes that underlie emotions are almost imperceptible to consciousness (Christou-Champi, Farrow, & Webb, 2015). Fortunately, automatic processing is related to better neural processing (Christou-Champi et al., 2015), so future researchers should use a variety of measures, including psychological and physiological approaches, to measure emotional arousal objectively. Third, researchers could examine other online emotional regulation strategies, such as attention deployment and cognitive change, in order to provide better scientific evidence to guide the development of prevention and intervention programs for cyber aggression.


The authors acknowledge support from the Research Foundation for Advanced Talents (WGKQ201702021) and the Provincial Support Program for Excellent Young Scholars (gxyq2018058) at West Anhui University. They wish to thank all the study participants, their parents and guardians, and the investigators involved in this research.


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Dengfeng Xie (1), Zhangming Xie (1)

(***) Education Science College, West Anhui University, People's Republic of China

CORRESPONDENCE Dengfeng Xie, No. 1 Yunlu Road, Yu'an District, Lu'an City, Anhui Province, People's Republic of China. Email:
Table 1. Multiple Regression Coefficients for Seeking Online Social
Support and Online Anger and Their Interaction Effect

Predictor                                      Unstandardized
                                               B      SE

Step 1          Seeking online social support  -0.02  0.02
                Online anger                    0.20  0.02
Step 2          Seeking online social support  -0.04  0.02
                Online anger                    0.22  0.02
Seeking online social support x online anger   -0.04  0.02

Predictor                                      Standardized
                                               [beta]  t

Step 1          Seeking online social support  -.04    -0.903 (**)
                Online anger                    .37     8.652 (**)
Step 2          Seeking online social support  -.17    -2.83
                Online anger                    .41     9.155 (**)
Seeking online social support x online anger   -.17    -2.827 (*)

Note. N = 509.

(*) p < .01, (**) p < .001.
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Author:Xie, Dengfeng; Xie, Zhangming
Publication:Social Behavior and Personality: An International Journal
Date:Jun 1, 2019
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