Skip to main content
PMC home page
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2022 May 11;227:103612. doi: 10.1016/j.actpsy.2022.103612

The relationship between internet addiction and aggressive behavior among adolescents during the COVID-19 pandemic: Anxiety as a mediator

Yifan Zhang a, Zhe Hou b, Song Wu c, Xiaoqian Li a, Mengyao Hao d, Xiaofei Wu a,⁎
PMCID: PMC9091340  PMID: 35598380

Abstract

The COVID-19 pandemic has brought unprecedented challenges for adolescents, who tended to experience more emotional instability, impulsivity, and aggressive behavior driven by the fear of infection and the uncertainty of network information. In the present study, we investigated the relationship between Internet addiction and aggressive behavior, and the mediating effects of depression and anxiety. There were differences in Internete addiction and aggressive behavior in gender, thus the moderating role of gender between them were explored. A total of 1148 middle school students were invited to complete the Buss Perry Aggression Questionnaire, the Internet Addition Scale, the Self-rating Depression Scale (SDS), and the Self-rating Anxiety Scale (SAS) separately. The results suggested that 1) there was a significant positive correlation between Internet addiction and aggressive behavior; 2) anxiety, but not depression, mediated the effect of Internet addiction on aggressive behavior; 3) gender did not moderate the effect of Internet addiction on aggressive behavior. The practical implication of the current findings on boosting adolescents' mental health was discussed and further suggestions were provided.

Keywords: Internet addiction, Aggressive behavior, Anxiety, Depression, Mediator

1. Introduction

The outbreak of COVID-19 has brought huge damage to the society. The lifestyle transformation and fear of the pandemic may cause individuals' psychological distress and symptoms of mental illness, especially for young adolescents. As adolescents are immature in their psychological development, they may develop a series of inevitable psychological and emotional distress such as anxiety, depression, and fear in the face of an outbreak (Chen et al., 2020). Given the policy of blockade the pandemic had restricted people's daily going out and the public's Internet use time became longer, which tended to cause some mental health problems. These negative emotions might lead to negative behaviors, even aggressive behaviors, which had a bad impact on public life.

1.1. Aggressive behavior

The Blockade policy of the pandemic had effectively controlled the spread of the virus. It gave people more time to get along with their families. However, as the time became longer, the possibility of conflict increased. Studies found that during the period of COVID-19, domestic violence and other incidents were frequent, and people's aggression increased (Mazza et al., 2020). Killgore et al. (2021) compared the aggressiveness of the public in lockdown and non-lockdown status, and found that people who was restricted from outgoing showed a higher level of aggressiveness. Especially for young adults, their aggressive behavior increased as well during the pandemic (Parola et al., 2020; Wang et al., 2021; Ye et al., 2021). For example, Wang et al. (2021) found that students in primary and middle school showed increased aggressive behaviors during the COVID-19 pandemic.

Moreover, studies revealed that the males were more aggressive than females (Buss & Perry, 1992; Hongyu & Lei, 2006), especially in terms of the physical aggression behavior, whereas there was generally no gender difference in hostility or anger (Fengshi & Shuhong, 2019; Junsheng et al., 2009). However, other studies showed that females had higher levels of anger expression than males (Chuanmei, 2020; Hejing, 2018).

1.2. Internet addiction

Due to the concern and fears of the pandemic, the public spends a lot of time focusing on relevant information over the Internet. Given that most students had to study online, their network usage time greatly increased, accompanied by the high risk of Internet addiction (Sun et al., 2020). Lin (2020) investigated the Internet addiction among 1060 junior high school students, and found that the detection rate of Internet addiction was 24.4%, which was higher than that of samples of the same age before the pandemic. Similarly, with a sample of 2050 children and adolescents, Dong et al. (2020) found that the prevalence rate of Internet addiction increased as the pandemic went on.

In addition, there was a difference in gender in Internet addiction. Males showed higher Internet Addiction rate than females (Fam, 2018; Zhao Yanqing et al., 2011), which might be attributed to the social expectations on different genders (Na, 2016).

1.3. Mental health under pandemic situation

During the pandemic, People showed obvious emotions such as anxiety, depression, compulsion and fear, among which anxiety and depression were the most common manifestations. Rong et al. (2020) conducted a sampling survey throughout China in the early stage of the pandemic and found that among 3087 respondents, the detection rate of anxiety symptoms was 13.35% and the detection rate of depression was 14.09%. Dan et al. (2020) investigated the mental health status of the national people during the outbreak of the pandemic and found that among the 14,592 respondents, the detection rate of depressive symptoms was 53.5% and the detection rate of anxiety symptoms was 44.6%. The results might vary when different sample sizes were surveyed with different measurement tools in different periods, but the mental health status of the people reflected by the existing survey was worrying.

1.4. The relationship among aggressive behavior, internet addiction, anxiety and depression

1.4.1. The relationship between aggressive behavior and Internet addiction

Previous studies showed that Internet overuse was strongly associated with aggression in adolescents (Kim, 2013; Obeid et al., 2019). Dhaka and Naris (2019) explored the relationship between Internet addiction and aggressive behavior among the university students and found a positive correlation between the two variables. Lim et al. (2015) collected 714 students' data and found those who with IAD (Internet addiction disorder) were prone to aggressive behaviors. Internet had the characteristics of anonymity, so people would become more individualistic and reduced their self-awareness when using the Internet, resulting in increased aggression. Furthermore, when using the Internet, people would be exposed to some violent games and other related contents, which would trigger their negative emotions or thoughts, as well as aggressive behaviors (Obeid et al., 2019). For high school students, in the face of great academic pressure and changes in adolescence, they would have higher feeling seeking, which would positively predict aggressive behavior through online game addiction (Chenxi, 2022).

Taken together, there is a close relationship between aggressive behavior and Internet addiction. Furthermore, according to previous studies, males and females differ in aggressive behavior and Internet addiction, thus we assume that gender may play a moderating role on the relationship between aggressive behavior and Internet addiction.

1.4.2. The relationship among aggressive behavior, anxiety and depression

Previous studies reported close associations between anxiety, depression and aggressive behavior (Roberts et al., 2010; Tanaka et al., 2010). Chung et al. (2019) divided students into anxiety group and control group, then compared their aggression scores. The result demonstrated that anxiety group showed more aggression than control group. A study of 50 families by Tanaka et al. (2010) showed that children with high anxiety levels showed higher aggression and more family conflicts.

Depression is also one of the emotions closely related to aggressive behavior. The positive correlation between depression and explicit aggression had been investigated by Wanyehe and Yuede (2018). A study by Roberts et al. (2010) showed that depression was associated with verbal aggression. In general, there was a complex correlation between anxiety, depression and aggression.

1.4.3. The relationship among Internet addiction, anxiety and depression

Studies showed that Internet addicts had higher anxiety (Yang et al., 2007) and depression levels (Obeid et al., 2019) than non addicts (Min-fang, 2008). It has been revealed that the moderate use of computers and mobile phones during the COVID-19 pandemic period was helpful to alleviate the anxiety symptoms, but long time would increase the risk of anxiety (Tianhao et al., 2020) and depression symptoms (Neill et al., 2021; Yi et al., 2020). Chen et al. (2020) indicated that electronic entertainment was positive associated with anxiety and depression among adolescents during the outbreaks. In sum, there was a close relationship between Internet addiction and anxiety and depression.

1.5. Aim and hypothesis

In the present study, we established a mediation model to explore the relationship among Internet addiction, aggressive behavior, anxiety and depression during the COVID-19 pandemic period. In addition, Internet addiction and aggressive behavior showed significant differences in gender, so we also explored the moderating role of gender between them. We hypothesized that: 1) Aggressive behavior during the COVID-19 was positively related to Internet addiction, anxiety and depression; 2) Anxiety and depression mediated the effects of Internet addiction on aggressive behavior during the pandemic period; 3) Gender moderated the effects of Internet addiction on aggressive behavior.

2. Method

2.1. Participants

A total of 1148 students in high schools located in Shanxi Province, China, were invited to complete a set of online questionnaires. All of them provided with written informed consent. After deleting 149 invalid data with regular rhythmic or incomplete responses (the exclusion criteria:1. all fill in the same option; 2. the missing option exceeds 20%), the data utilized for further analysis was 999 (485 females), and the valid recovery rate was 87.02%. The average age of the sample was 17.01 ± 0.77 years. The distribution of participants in the current study is shown in Table 1 . All participants were asked to provide information on demographic variables, frequency of network usage, Internet addiction, aggression, anxiety and depression.

Table 1.

Demographic distribution of the participants.

Male Female Total Mean Age
Senior 1 293 273 566 16.92
Senior 2 221 212 433 17.13
Total 514 485 999
Open in a new tab

2.2. Measures and procedure

All data were collected in July 2020 through a web-based survey. With the help of teachers, students filled in the questionnaire online using computers in the computer room of the school. Each questionnaire has clear instructions, which guided students to answer each question. All students signed an informed consent form before filling in the questionnaire.

The self-report questionnaires used in this study were the:

·Basic information table, which including demographic variables, and frequency of network usage.

·Buss Perry Aggression Questionnaire (Buss & Perry, 1992).

·Internet Addiction Scale (Min-fang, 2008).

·The Self-rating Anxiety Scale (Zung, 1965).

·The Self-rating Anxiety Scale (Zung, 1971).

2.2.1. Aggressive behavior

Buss Perry Aggression Questionnaire is a self-rating scale composed of 29 items presented in Likert-5. The score ranging from 1 (completely inconsistent) to 5 (completely consistent). There are four subscales: physical aggression (9 items), verbal aggression (5 items), anger (7 items), and hostility (8 items). Examples of items were, “I will use force to protect my rights when necessary” (physical aggression), “When I disagree with my friends, I will tell them publicly” (verbal aggression), “My friends think I am a man with a bad temper” (anger) and “When people are especially nice to me, I doubt their real purpose” (hostility). The total score denotes an index of trait aggression. The Cronbach's α coefficient was 0.912. The Cronbach's α coefficient of physical aggression, verbal aggression, anger, hostility subscales were 0.777, 0.666, 0.715 and 0.834 respectively.

2.2.2. Internet addiction

Internet addiction was assessed by the Internet Addiction Scale, which is a self-rating scale composed of 28 items and is scored on 0 (No) or 1 (Yes). Factor analysis of the items showed there are two subscales: addictive symptoms (6 items about tolerance, 6 items about withdrawal response) and Internet addiction related problems (5 items about Interpersonal relationship, 8 items about time management). Examples of items are, “I tried to spend less time on the Internet, but I failed” (tolerance), “I feel uncomfortable as long as I don't surf the Internet for a period of time” (withdrawal response), “Due to the Internet, I have spent less time with my parents, teachers and classmates” (Interpersonal relationship) and “More than once, I had less than five hours of sleep because of the Internet” (time management). The total score denotes an index of Internet addiction and the higher scores indicate higher possibility of Internet addiction. The Cronbach's α coefficient was 0.918. We calculated a composite score of the Internet addiction, following the rules in previous studies which treated the Internet addiction as one unitary dimension (Lin, 2020; Min-fang, 2008).

2.2.3. Depression

The Self-rating Depression Scale (SDS) is a 20-item self-report scale, which is scored on 4-point ranging from 1 (occasionally) to 4 (constantly). Examples of items were, “I feel down-hearted and blue” and “I have crying spells or feel like it”. Higher scores indicate higher levels of depression. The Cronbach's α coefficient was 0.796.

2.2.4. Anxiety

The Self-rating Anxiety Scale (SAS) was utilized to assess anxiety. The SAS consists of 20 questions that assess how participants felt during the previous week. Participants need to answer each question on a Likert-4 scale. The score ranging from 1 (occasionally) to 4 (constantly). Examples of items were, “I feel more nervous or anxious than usual” and “I am scared for no reason”. Higher scores indicate higher levels of anxiety. The Cronbach's α coefficient was 0.829.

2.3. Data analyses

SPSS 24.0 and PROCESS macro for SPSS were used to analyze the data. First, we computed descriptive statistics to assess the demographic characteristics. Second, correlation analysis was used to analyze the correlation between variables. Third, t-test was used to analyze gender and grade differences. Fourth, we used model 1 to test the mediating effect of depression and anxiety, and model 4 to test the moderating effect of gender on the relationship between Internet addiction and aggressive behavior. The bootstrap method was used, and the bias-corrected 95% confidence intervals of 5000 resamples of the data were detected.

3. Result

3.1. Common method bias test

Since the self-report questionnaires were adopted, there may be common method bias (Podsakoff et al., 2003). Therefore, we used the Harman single-factor test to solve the issue. All the variables measurement items in unrotated factor analysis were combined. If only one factor remains or the first factor explains the vast majority of the variation, there is severe common method bias. In addition, the proportion of the variation explained by the first principle component should not exceed 40%. A total of 19 factors were obtained and together explained 56.38% of the variance in the current study. The proportion of the variation explained by the first principle component was 18.19%, which did not exceed the critical value (40%). Thus, common method bias did not seriously affect the results in this study.

3.2. Descriptive statistics

The frequency of network usage was detailed in Table 2 . During the pandemic period, students frequently used mobile phones or other network tools, and more than half of them used the tools for over six hours a day. As shown in Table 5, network usage had a close association with Internet addiction (r = 0.16, p < 0.01). As expected, aggression was positively related to Internet addiction (r = 0.43, p < 0.01), anxiety (r = 0.39, p < 0,01) and depression (r = 0.32, p < 0.01). As shown in Table 3 , the results indicated that male students scored higher than females on Internet addiction [t (997) = 3.15, p < 0.01], physical aggression [t (997) = 3.98, p < 0.001]. However, no gender difference was found in aggression, verbal aggression, anger, hostility, anxiety and depression (all p > 0.05). The grade differences were showed in Table 4 . We found that grade 8 students scored higher than grade 7 students on physical aggression [t (997) = −2.21, p < 0.05], anxiety [t (997) = −2.68, p < 0.01]. However, no grade difference was found in Internet addiction, aggression, verbal aggression, anger, hostility and depression (all p > 0.05).

Table 2.

Frequency of network usage.

 Grade 7
 Grade 8
 Total
Male Female Male Female
Within 1 h 8 12 14 5 39
1–2 h 27 32 22 31 112
3–4 h 50 48 34 33 165
5–6 h 42 42 22 39 145
Above 6 h 166 139 129 104 538
Total 293 273 221 212 999
566 433
Open in a new tab

Table 5.

Correlations of Internet addiction, frequency of network usage, aggression, anxiety and depression.

Variables 1 2 3 4 5 6 7 8 9
1. Internet addiction 1
2. Frequency of network 0.16⁎⁎⁎ 1
3. Aggression 0.43⁎⁎⁎ 0.12⁎⁎⁎ 1
4. Physical aggression 0.36⁎⁎⁎ 0.09⁎⁎ 0.79⁎⁎⁎ 1
5. Verbal aggression 0.29⁎⁎⁎ 0.07⁎ 0.78⁎⁎⁎ 0.49⁎⁎⁎ 1
6. Anger 0.06 0.20 0.26⁎⁎⁎ 0.17⁎⁎⁎ 0.18⁎⁎⁎ 1
7. Hostility 0.13⁎⁎⁎ 0.08⁎ 0.26⁎⁎⁎ 0.17⁎⁎⁎ 0.16⁎⁎⁎ 0.61⁎⁎⁎ 1
8. Anxiety 0.29⁎⁎⁎ 0.06 0.39⁎⁎⁎ 0.34⁎⁎⁎ 0.19⁎⁎ 0.13⁎⁎⁎ 0.12⁎⁎⁎ 1
9. Depression 0.26⁎⁎⁎ 0.08⁎ 0.32⁎⁎⁎ 0.26⁎⁎⁎ 0.11⁎⁎⁎ 0.12⁎⁎⁎ 0.12⁎⁎⁎ 0.71⁎⁎⁎ 1
Open in a new tab

Note: N = 999.

⁎

p < 0.05.

⁎⁎

p < 0.01.

⁎⁎⁎

p < 0.001.

Table 3.

Gender differences in internet addition, aggression, physical aggression, verbal aggression, anxiety, anger, hostility, anxiety and depression.

Total (N = 999)
 Male (n = 514)
 Female (n = 485)
M(SD) M(SD) M(SD) t p
Internet addiction 7.85(6.73) 8.50(7.33) 7.17(5.98) 3.15 0.002⁎⁎
Aggression 72.78(18.07) 72.94(18.21) 72.61(17.93) 0.29 0.772
Physical aggression 18.69(5.32) 19.34(5.49) 18.01(5.05) 3.98 <0.001⁎⁎⁎
Verbal aggression 12.82(3.82) 12.83(3.99) 12.81(3.65) 0.08 0.939
Anger 15.27(5.61) 15.23(5.61) 15.31(5.61) −0.22 0.823
Hostility 20.82(6.71) 21.13(6.56) 20.49(6.87) 1.49 0.136
Anxiety 35.38(6.58) 35.16(6.56) 35.62(6.61) −1.10 0.273
Depression 42.23(6.90) 41.84(6.93) 42.65(6.85) −1.84 0.066
Open in a new tab

Note.

⁎⁎

p < 0.01.

⁎⁎⁎

p < 0.001.

Table 4.

| Grade differences in Internet addition, aggression, physical aggression, verbal aggression, anxiety, anger, hostility, anxiety and depression.

Total (N = 999)
 Grade 7 (n = 566)
 Grade 8(n = 4 33)
M(SD) M(SD) M(SD) t p
Internet addiction 7.85(6.73) 7.96(6.45) 7.71(7.09) 0.59 0.552
Aggression 72.78(18.07) 72.25(18.08) 73.47(18.06) −1.06 0.289
Physical aggression 18.69(5.32) 18.37(5.21) 19.12(5.44) −2.21 0.028⁎
Verbal aggression 12.82(3.82) 12.67(3.80) 13.01(3.85) −1.39 0.166
Anger 15.27(5.61) 15.40(5.60) 15.09(5.62) 0.88 0.379
Hostility 20.82(6.71) 20.85(6.66) 20.78(6.79) 0.18 0.860
Anxiety 35.38(6.58) 34.89(6.34) 36.02(6.84) −2.68 0.007⁎⁎
Depression 42.23(6.90) 42.18(7.08) 42.30(6.66) −0.26 0.792
Open in a new tab

Note.

⁎

p < 0.05.

⁎⁎

p < 0.01.

3.3. Mediating effect test

Regression analysis was used. All data were standardized. The result was shown in Table 6. Anxiety (β = 0.39, p < 0.001), depression (β = 0.32, p < 0.001) and Internet addiction (β = 0.43, p < 0.001) were all significant predictors of aggression. To examine the mediation hypothesis, this study used the PROCESS macro (model 4) to estimate the model parameters. Internet addiction, aggression, anxiety and depression were entered into the model. Gender and grade as covariates to control their influences on the results. The regression analysis (Table 7) showed that the total indirect effect for two mediators was 0.24, and the 95% confidence intervals [0.17, 0.32] did not include zero. Therefore, the mediating effect was significant. In the path of Internet addiction → anxiety → aggression, the mediating effect of anxiety was 0.21, and the confident interval was [0.13, 0.30], which indicated that, when anxiety acts as a mediator, the mediating path was established and it played a partial mediating role in this path. In the path of Internet addiction → depression → aggression, the mediating effect of depression was 0.04, and the confidence interval was [−0.02, 0.10], including zero; thus, the mediating path was not established, and depression had no mediating effect in Internet addiction and aggression of senior high school students. Taken together, we could conclude that anxiety played a mediating role in the relationship between Internet addiction and aggression among high school students, while depression did not. The results are shown in Fig. 1 .

Table 6.

Regression of aggression with anxiety, depression and Internet addiction.

Dependent Variable Predictive Variable β t F R2 △R2
Aggression Anxiety 0.39 13.50⁎⁎⁎ 182.34⁎⁎⁎ 0.16 0.15
Depression 0.32 10.72⁎⁎⁎ 114.90⁎⁎⁎ 0.10 0.10
Internet addiction 0.43 14.82⁎⁎⁎ 219.64⁎⁎⁎ 0.18 0.18
Open in a new tab

Note.

⁎⁎⁎

p < 0.001.

Table 7.

Mediation analysis of anxiety and depression in the relationship between Internet addiction and aggression.

Effect Boot SE LLCI ULCI
Direction effect
Internet addiction → aggression 0.91 0.08 0.76 1.06
Indirect effect
Anxiety 0.21 0.04 0.13 0.30
Depression 0.04 0.03 −0.02 0.10
Total 0.24 0.04 0.17 0.32
Open in a new tab

Note: 5000 bootstrap samples; N = 999; standardization of all variables.

Fig. 1.

Fig. 1

Open in a new tab

The mediating path of anxiety and depression between Internet addiction and physical aggression.

⁎⁎⁎p < 0.001 (standardized regression coefficients with 5000 Bootstrap Samples).

3.4. Moderating effect test

To examine the moderation hypothesis, this study used PROCESS macro (model 1) to estimate the model parameters. According to the results of independent sample t-test, only the two variables of Internet addiction and physical aggression showed differences in gender, so we further explored the moderating role of gender between Internet addiction and physical aggression, and established a simple moderation model. The continuous variables were centralized. Internet addiction, physical aggression and gender were put into the model. The results were shown in Table 8, the interaction effect between Internet addiction and gender on physical aggression was not significant (β = −0.002, p > 0.05). Therefore, the moderating effect on this path was not significant. Gender didn't play a moderating role between Internet addiction and physical aggression.

Table 8.

Moderation analysis of gender in the relationship between Internet addiction and physical aggression.

Physical aggression
Effect SE t p
Gender −0.96 0.31 −3.04 <0.01
Internet addiction 0.28 0.07 4.02 <0.001
Gender × internet addiction −0.002 0.05 −0.04 0.97
R2 0.00
F 0.001
p 0.97
Open in a new tab

We further explored the role between Internet addiction and physical aggression, and analyzed the mediating role of anxiety in the relationship between Internet addiction and physical aggression by gender separately. PROCESS macro (model 4) was used to establish a simple mediation model. The result was shown in Table 9, anxiety played a partial mediating role in Internet addiction and physical aggression for both males (β = 0.06; CI [0.03, 0.09]) and females (β = 0.07; CI [0.03, 0.12]). The results revealed that Internet addiction partially affected physical aggression through anxiety both in male and female.

Table 9.

Mediation analysis of anxiety in the relationship between Internet addiction and physical aggression in gender.

Effect Bootstrap SE LLCI ULCI
Male
Direct effect
 Internet addiction → physical aggression 0.22 0.03 0.16 0.29
Indirect effect
 Anxiety 0.06 0.02 0.03 0.09
 Total 0.06 0.01 0.03 0.09



Female
Direct effect
 Internet addiction → physical aggression 0.21 0.04 0.13 0.28
Indirect effect
 Anxiety 0.07 0.02 0.03 0.12
 Total 0.07 0.02 0.04 0.11
Open in a new tab

Note: 5000 bootstrap samples; Male, n = 514; Female, n = 485; standardization of all variables.

4. Discussion

Anxiety and fear brought about by COVID-19 pandemic caused more impulsive and aggressive behaviors, especially among adolescents. The present study explored the causes and underlying mechanisms behind adolescent aggressive behavior during the COVID-19 pandemic. The results suggested that adolescents' aggressive behavior during the COVID-19 was positively related to Internet addiction, anxiety and depression. Furthermore, anxiety but not depression mediated the effects of Internet addiction on aggressive behavior during the pandemic period. Besides, we explored the moderating role of gender between Internet addiction and aggressive behavior. The results showed differences in Internet addiction and physical aggression in terms of gender, but the gender did not play the moderator role. These findings provided additional evidence on the relationship between Internet addiction and aggression, and showed the mediating effect of anxiety among adolescents during the COVID-19.

4.1. The relationship between internet addiction and aggressive behavior during the COVID-19 pandemic

During the COVID-19 pandemic, the aggression of adolescents was increased (Killgore et al., 2021; Parola et al., 2020). Individuals, especially adolescents showed greater tendencies to lose temper, verbally or even physically attack others when faced with negative events. The influence of Internet information and media is one of the most key factors for adolescents' aggressive behaviors.

Out of fear of infection and uncertainty of information, high school students spent longer time on the Internet. With the increase of Internet usage time, the possibility of Internet addiction was also Increased.

In the current study, the correlational analyses revealed that there was a significant positive correlation between Internet addiction and aggressive behavior of high school students during the pandemic. Through further regression analysis, we found that Internet addiction could significantly predict aggression. These results were consistent with previous studies research which found that adolescents with internet addiction, especially those in high school, were more inclined to have aggressive behaviors, and Internet addiction may strengthen their aggressive behaviors (Kim et al., 2008; Ko et al., 2012; Ko et al., 2009; Park et al., 2013). Therefore, during the COVID-19 pandemic, the excessive use of Internet, or using the Internet addictively, may easily leads to aggressive behaviors. The fear of the pandemic and the panic caused by the spread of fake news have made many netizens emotionally unstable, resulting in a chaotic and hostile network environment. In addition, the network has the characteristics of anonymity, which may stimulate peoples' tendency of aggression, especially for high school students whose emotions are unstable.

4.2. The mediating role of anxiety between internet addition and aggressive behavior

We found that anxiety played a mediating role between Internet addiction and aggression in teenagers. Berkowitz (1989) proposed the reformulated frustration-aggression hypothesis, which suggested that if a desired goal was frustrated, individuals were inclined to have a negative emotion and further led to verbal and physical aggressive behaviors.

In the present study, students' increasingly anxious emotion during the pandemic might be a very important reason for aggressive behavior (Chen et al., 2020; Tang et al., 2021). Longstanding media exposure, particularly in the period of public emergencies, would aggregate one's anxiety (Alasousi et al., 2020; Wheaton et al., 2012). During the pandemic, there were mass information about the virus published on the Internet every day. However, it was mixed with a lot of fake information, which might cause people's misunderstanding and panic (Yi et al., 2020). Especially for the high school students with insufficient judgment ability, blindly browsing the relevant information and credulous rumors would cause their anxiety.

Moreover, some students might not adapt to online teaching (Huan et al., 2020), which caused low learning efficiency and further led to academic anxiety.

In summary, due to the fear and uncertainty of the pandemic, adolescents were inclined to search on the Internet for information. However, the complex network information triggered adolescent anxiety, which further led to aggressive behaviors.

Moreover, the present study implied that although there was a significant correlation between depression and aggressive behaviors, depression did not act as a mediator. Previous studies found that individuals with depression symptoms reported more Internet addiction and aggressive behaviors (Pettit, 1997; Yen et al., 2007). Although anxiety and depression could be the two salient negative emotions generated during the pandemic, it is more likely that the panic over the pandemic and the uncertainty of mass online information led to more anxiety than depression among adolescents, and the difficulty of anxiety release further led to aggressive behaviors.

4.3. The moderating role of gender between internet addition and aggression

We found that there were differences in Internet addiction and aggression between males and females. These results were consistent with previous studies (Junsheng et al., 2009; Fengshi & Shuhong, 2019). Males were significantly higher than females in Internet addiction and physical aggression, but there was no significant difference in the dimension of overall aggression which was inconsistent with previous studies (Buss & Perry, 1992; Hongyu & Lei, 2006). We further established a gender moderation model between Internet addiction and physical aggression, and found that the moderation effect was not significant. Thus, the moderation model was not established.

The influence of Internet addiction on physical aggression had been further explored. We found that neither for males nor for females, Internet addiction would affect physical aggression through anxiety. This revealed that the effect of Internet addiction on physical aggression may be achieved through the same underlying mechanism in males and females, and anxiety may be one of the explanations for the effect.

4.4. Suggestion

Based on our findings, we suggest that 1) The government should establish an official information website and update and release accurate information about the pandemic situation on the website in time. In addition, government should strengthen the supervision and management of network media public opinion in public emergencies during the COVID-19 pandemic, and create a healthy network environment to reduce public, especially adolescents' anxiety and panic; 2) Adolescents should use the Internet rationally, control the time of suffering the Internet, and examine the information on the Internet. Adolescents should not believe or spread rumors. Pay more attention to themselves mental health and ask parents and teachers for help in time when they need. Find a way to vent reasonably negative emotions, such as reading, indoor sports, listening to songs or making a diary; 3) In public emergencies, counseling psychologists should provide professional guidance and help to the public, especially teenagers. Such as setting up psychological hotlines, spreading science psychological knowledge. Counseling psychologists should help the public reduce their negative emotions such as anxiety, and guide to correctly identify, manage and regulate their emotions in the stress events.

4.5. Limitations and future research

In this study, there was no follow-up study on the situation of high school students, and we couldn't determine whether students had Internet addiction before. In addition, the pandemic situation in each place was different. This study investigated high school students in Shanxi, which was not representative of the whole country. It can be used as a reference to explore the relationship between Internet addiction and aggression of adolescents in low-risk areas.

The pandemic situation continues. This research in the pandemic situation has great reference value for the psychological assistance to the public in public health emergencies. Future research can carry out follow-up research to explore the relationship between public psychological status, Internet addiction and aggression under different pandemic situations. In addition, future researchers can also explore the situation of the public in different risk areas and conduct cross-sectional exploration. In this way, when we encounter public health emergencies, we can take actions more appropriately and quickly to protect the public's mental health.

5. Conclusion

In summary, the present research suggested that Internet addiction was an important cause of adolescents' aggressive behavior during the COVID-19 pandemic. In addition, the mediation analysis showed that anxiety mediated the effect of Internet addiction and aggressive behavior, which was consistent with the reformulated frustration-aggression hypothesis. Our findings implied that interventions on adolescent aggressive impulsivity/behaviors should pay attention to Internet use during the COVID-19.

Funding

The research was supported by the Natural Science Foundation of Zhejiang Province (LQ22C090001) and National Natural Science Foundation of China (32100836).

CRediT authorship contribution statement

XW designed the experiment and supervised the research procedure. YZ performed the experiment. YZ analyzed the data. YZ, ZH, SW, XL and MH wrote the manuscript. XW approved the final submission. XW, YZ, ZH, SW, XL and MH revised the manuscript.

Declaration of competing interest

The authors have no potential conflicts of interest.

References

  1. Alasousi L., Al Hammouri S., Al-abdulhadi S.A. Anxiety and media exposure during COVID-19 outbreak in Kuwait [Preprint] Primary Care Research. 2020 [Google Scholar]
  2. Berkowitz L. Frustration-aggression hypothesis: Examination and reformulation. Psychological Bulletin. 1989;106(1):59–73. doi: 10.1037/0033-2909.106.1.59. [DOI] [PubMed] [Google Scholar]
  3. Buss A.H., Perry M. The aggression questionnaire. Journal of Personality and Social Psychology. 1992;63(3):452–459. doi: 10.1037//0022-3514.63.3.452. [DOI] [PubMed] [Google Scholar]
  4. Chen F., Zheng D., Liu J., Gong Y., Guan Z., Lou D. Depression and anxiety among adolescents during COVID-19: A cross-sectional study. Brain, Behavior, and Immunity. 2020;88:36–38. doi: 10.1016/j.bbi.2020.05.061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Na C. China West Normal University; 2016. Research on the relationship between parental control, coping style and internet addiction in middle school students. master, China West Normal University. [Google Scholar]
  6. Chung J.E., Song G., Kim K., Yee J., Kim J.H., Lee K.E., Gwak H.S. Association between anxiety and aggression in adolescents: A cross-sectional study. BMC Pediatrics. 2019;19(1):115. doi: 10.1186/s12887-019-1479-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Dhaka P., Naris C. In: Data science and big data analytics. Mishra D.K., Yang X.-S., Unal A., editors. Springer Singapore; Singapore: 2019. A study of the correlation between internet addiction and aggressive behaviour among the Namibian University students; pp. 1–9. [Google Scholar]
  8. Dong H., Yang F., Lu X., Hao W. Internet addiction and related psychological factors among children and adolescents in China during the coronavirus disease 2019 (COVID-19) epidemic. Frontiers in Psychiatry. 2020;11:00751. doi: 10.3389/fpsyt.2020.00751. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Fam J.Y. Prevalence of internet gaming disorder in adolescents: A meta-analysis across three decades. Scandinavian Journal of Psychology. 2018;59(5):524–531. doi: 10.1111/sjop.12459. [DOI] [PubMed] [Google Scholar]
  10. Fengshi H., Shuhong S. The Guide of Science & Education. Vol. 16. 2019. A study on childhood trauma, empathy and aggressive behavior of college students of different genders; pp. 164–165. [Google Scholar]
  11. Chuanmei H. Qingdao University; 2020. The Relationship Between Adolescents’ Emotional Intelligence, Moral Disengagement and Aggressive Behavior. Qingdao University. [Google Scholar]
  12. Rong J., Kangxing S., Fengying B., Fengsu H., Shiyan Y., Rui X., Dan L. Psychological status among inhabitants during early epidemic stage of COVID-19. Academic Journal of Chinese PLA Medical School. 2020;41(09) 947-951+953. [Google Scholar]
  13. Killgore W.D.S., Cloonan S.A., Taylor E.C., Anlap I., Dailey N.S. Increasing aggression during the COVID-19 lockdowns. Journal of Affective Disorders Reports. 2021;5 doi: 10.1016/j.jadr.2021.100163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Kim E.J., Namkoong K., Ku T., Kim S.J. The relationship between online game addiction and aggression, self-control and narcissistic personality traits. European Psychiatry. 2008;23(3):212–218. doi: 10.1016/j.eurpsy.2007.10.010. [DOI] [PubMed] [Google Scholar]
  15. Kim K. Association between internet overuse and aggression in Korean adolescents: Internet overuse and aggression. Pediatrics International. 2013;55(6):703–709. doi: 10.1111/ped.12171. [DOI] [PubMed] [Google Scholar]
  16. Ko C.H., Yen J.Y., Yen C.F., Chen C.S., Chen C.C. The association between internet addiction and psychiatric disorder: A review of the literature. European Psychiatry: The Journal of the Association of European Psychiatrists. 2012;27(1):1–8. doi: 10.1016/j.eurpsy.2010.04.011. [DOI] [PubMed] [Google Scholar]
  17. Ko C.-H., Yen J.-Y., Liu S.-C., Huang C.-F., Yen C.-F. The associations between aggressive behaviors and internet addiction and online activities in adolescents. Journal of Adolescent Health. 2009;44(6):598–605. doi: 10.1016/j.jadohealth.2008.11.011. [DOI] [PubMed] [Google Scholar]
  18. Chenxi L. Hebei Normal University; 2022. The Effect of Sensation Seeking on Aggressive Behavior of Senior High Students : The Chain Mediating Effect of Bad Peer Interaction and Online Game Addiction (Hebei Normal University). [Google Scholar]
  19. Hongyu L., Lei F. Psychological Development and Education. 02. 2006. The development of high-school students’ aggressiveness. [Google Scholar]
  20. Wanyehe L., Yuede C.u. Abstracts of the 21st Chinese Psychological Society. 2018. A study of relationship on parenting styles and explicit aggression of middle school students: The mediating role of depression; p. 2. [Google Scholar]
  21. Lim J.-A., Gwak A.R., Park S.M., Kwon J.-G., Lee J.-Y., Jung H.Y., Choi J.-S.… Are adolescents with internet addiction prone to aggressive behavior? The mediating effect of clinical comorbidities on the predictability of aggression in adolescents with internet addiction. Cyberpsychology, Behavior and Social Networking. 2015;18(5):260–267. doi: 10.1089/cyber.2014.0568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Lin M.-P. Prevalence of internet addiction during the COVID-19 outbreak and its risk factors among junior high school students in Taiwan. International Journal of Environmental Research and Public Health. 2020;17(22):8547. doi: 10.3390/ijerph17228547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Dan L., Yanping R., Li Yuqing Y., Wei X., .Q., Zhiren W., Shuping T.… Survey on mental health status of China residents during the outbreak of new coronavirus pneumonia. Chinese Journal of Psychiatry. 2020;53(03):181–189. [Google Scholar]
  24. Junsheng L., Ying Z., Wenyu G. Reliability and validity of Chinese version of Buss-Perry aggression questionnaire in adolescents. Chinese Journal of Clinical Psychology. 2009;17(04):449–451. [Google Scholar]
  25. Hejing M. Chongqing Normal University; 2018. Research on the characteristics and related factors of students’ aggression in primary and middle school (Chongqing Normal University) [Google Scholar]
  26. Mazza M., Marano G., Lai C., Janiri L., Sani G. Danger in danger: Interpersonal violence during COVID-19 quarantine. Psychiatry Research. 2020;289 doi: 10.1016/j.psychres.2020.113046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Neill R.D., Blair C., Best P., McGlinchey E., Armour C. Media consumption and mental health during COVID-19 lockdown: A UK cross-sectional study across England, Wales, Scotland and Northern Ireland. Journal of Public Health. 2021:1–9. doi: 10.1007/s10389-021-01506-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Obeid S., Saade S., Haddad C., Sacre H., Khansa W., Al Hajj R., Hallit S.… Internet addiction among Lebanese adolescents: The role of self-esteem, anger, depression, anxiety, social anxiety and fear, impulsivity, and aggression—A cross-sectional study. Journal of Nervous & Mental Disease. 2019;207(10):838–846. doi: 10.1097/NMD.0000000000001034. [DOI] [PubMed] [Google Scholar]
  29. Park S., Hong K.-E.M., Park E.J., Ha K.S., Yoo H.J. The association between problematic internet use and depression, suicidal ideation and bipolar disorder symptoms in Korean adolescents. The Australian and New Zealand Journal of Psychiatry. 2013;47(2):153–159. doi: 10.1177/0004867412463613. [DOI] [PubMed] [Google Scholar]
  30. Parola A., Rossi A., Tessitore F., Troisi G., Mannarini S. Mental health through the COVID-19 quarantine: A growth curve analysis on Italian young adults. Frontiers in Psychology. 2020;11 doi: 10.3389/fpsyg.2020.567484. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Pettit G.S. The developmental course of violence and aggression: Mechanisms of family and peer influence. Psychiatric Clinics of North America. 1997;20(2):283–299. doi: 10.1016/s0193-953x(05)70313-0. [DOI] [PubMed] [Google Scholar]
  32. Podsakoff P.M., MacKenzie S.B., Lee J.-Y., Podsakoff N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology. 2003;88(5):879–903. doi: 10.1037/0021-9010.88.5.879. [DOI] [PubMed] [Google Scholar]
  33. Roberts S.J., Glod C.A., Kim R., Hounchell J. Relationships between aggression, depression, and alcohol, tobacco: Implications for healthcare providers in student health. Journal of the American Academy of Nurse Practitioners. 2010;22(7):369–375. doi: 10.1111/j.1745-7599.2010.00521.x. [DOI] [PubMed] [Google Scholar]
  34. Sun Y., Li Y., Bao Y., Meng S., Sun Y., Schumann G., Shi J.… Brief report: Increased addictive internet and substance use behavior during the COVID-19 pandemic in China. The American Journal on Addictions. 2020;29(4):268–270. doi: 10.1111/ajad.13066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Tanaka A., Raishevich N., Scarpa A. Family conflict and childhood aggression: The role of child anxiety. Journal of Interpersonal Violence. 2010;25(11):2127–2143. doi: 10.1177/0886260509354516. [DOI] [PubMed] [Google Scholar]
  36. Tang S., Xiang M., Cheung T., Xiang Y.-T. Mental health and its correlates among children and adolescents during COVID-19 school closure: The importance of parent-child discussion. Journal of Affective Disorders. 2021;279:353–360. doi: 10.1016/j.jad.2020.10.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Yi W., Junling G., Hao C., Yimeng M., Suhong C., Junming D., Hua F.… The relationship between media exposure and mental health problems during COVID-19 outbreak. Fudan University Journal of Medical Sciences. 2020;47(02):173–178. [Google Scholar]
  38. Wang X., Yanling L., Jie L., Yibo G. Analysis of aggression in primary and middle school students during COVID-19 pandemic and its influencing factors. Journal of Southwest University(Natural Science Edition) 2021;43(01):12–21. [Google Scholar]
  39. Huan W., Yaling P., Sigang Y., Yan Y. Investigation of knowledge, attitudes and mental health among high school students about COVID-19. China Journal of Health Psychology. 2020;28(12):1829–1833. [Google Scholar]
  40. Wheaton M.G., Abramowitz J.S., Berman N.C., Fabricant L.E., Olatunji B.O. Psychological predictors of anxiety in response to the H1N1 (Swine Flu) pandemic. Cognitive Therapy and Research. 2012;36(3):210–218. [Google Scholar]
  41. Tianhao W., Yi Z., Wang Renjie W., Xiaoyan T.S., Shaojun X., Fangbiao T. Relationship between digital media use and anxiety in college students during the COVID-19 outbreak. Chinese Journal of School Health. 2020;41(11):1625–1628. [Google Scholar]
  42. Yang Y., Hou Y., Zhang Q. Study on college students' internet addiction disorder and anxiety. Journal of Inner Mongolia Normal University (Philosophy and Social Sciences Edition) 2007;6:73–77. [Google Scholar]
  43. Ye B., Zeng Y., Im H., Liu M., Wang X., Yang Q. The relationship between fear of COVID-19 and online aggressive behavior: A moderated mediation model. Frontiers in Psychology. 2021;12:312. doi: 10.3389/fpsyg.2021.589615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Yen J.-Y., Ko C.-H., Yen C.-F., Wu H.-Y., Yang M.-J. The comorbid psychiatric symptoms of internet addiction: Attention deficit and hyperactivity disorder (ADHD), depression, social phobia, and hostility. Journal of Adolescent Health. 2007;41(1):93–98. doi: 10.1016/j.jadohealth.2007.02.002. [DOI] [PubMed] [Google Scholar]
  45. Yanqing Z., Yunxia W., Qingping C. Internet usage and internet addiction status of adolescents in Shaanxi Province. China Journal of Health Psychology. 2011;19(02):209–211. [Google Scholar]
  46. Min-fang Z. Soochow University; 2008. Internet addiction disorder of the junior high pupil to origin and countermeasure. master, Soochow University. [Google Scholar]
  47. Zung W.W.K. A self-rating depression scale. Archives of General Psychiatry. 1965;12(1):63. doi: 10.1001/archpsyc.1965.01720310065008. [DOI] [PubMed] [Google Scholar]
  48. Zung W.W.K. A rating instrument for anxiety disorders. Psychosomatics. 1971;12(6):371–379. doi: 10.1016/S0033-3182(71)71479-0. [DOI] [PubMed] [Google Scholar]

Articles from Acta Psychologica are provided here courtesy of Elsevier

RESOURCES