Numerous studies have found that addiction and depression often go hand in hand, and that addiction increases the risk of depression and is an important predictor of depression (Oh et al., 2013). A 4-year follow-up study confirms that Internet addiction is a stable cause of depression (Joseph et al., 2016). Therefore, in the development of depression, Internet addiction is considered to be a significant risk factor. In recent years, with the rapid development of mobile Internet, researchers have found that mobile phone addiction, as another important aspect of behavioral addiction after Internet addiction, is also significantly positively correlated with depression (Alhassan et al., 2018), which has potential effects on individual depression (Jun, 2016; Yang C. et al., 2019). Studies have examined the intermediation of emotional and cognitive factors between mobile phone addiction and depression (Li et al., 2017; Yang X. et al., 2019), but less attention to the indirect effects that mobile phone addiction may trigger related online behaviors such as cyberbullying. Previous studies have focused on mobile phone use and cyberbullying among college students (Bailin et al., 2014), and with the development of information society and economy, mobile phones have become more common among high school students, facilitating their lives while increasing their risk of cyberbullying (Mendez et al., 2020; Smale et al., 2021; Yadav and Chakraborty, 2021).

Based on these previous findings, the present study proposes hypothesis H1: High school students’ mobile phone addiction can lead to depression. That is, high school students addicted to mobile phones have a higher level of depression.

Mobile phone addiction is characterized by Inability to control craving, Feeling anxious and lost, Withdrawal/escape and Productivity loss (Leung, 2008). The study found that phone and Internet use for too long caused by Inability to control craving was a risk factor for cyberbullying (Kim et al., 2013; Alvarez-Garcia et al., 2015; HwaJin and Wanju, 2021). The Productivity loss of mobile phone addiction can lead to depression, anxiety, stress, sleep, and health problems in individuals, and increase the risk of individual cyberbullying (Sheinov, 2021). Social media addiction caused by Withdrawal/escape can also cause users to experience cyberbullying (Jain and Agrawal, 2020). Moreover, the compulsive examination of cell phones and cellphone vibration hallucinations caused by Feeling anxious and lost are related to the victimization and implementation of cyberbullying (Catone et al., 2020). Kowalski uses the General Aggressive Model to explain cyberbullying (Kowalski et al., 2014). GAM was based on cognitive knowledge structures (i.e., scripts and patterns) and integrating the development of cyberbullying through three parts: the input of individual and contextual factors, cognitive, emotional, and wake-up pathways that affect current internal states, and the assessment and decision-making processes that lead to resulting behaviors (Anderson and Bushman, 2002). Results from inputs enter the assessment and decision-making process through their impact on cognition, emotion, and wakefulness, both to determine the near-end process (focusing on the assessment and decision-making process in the context of cyberbullying) and to long-term negative outcomes for adolescents (e.g., depression, anxiety, behavioral problems, etc.) (Liu et al., 2020b,2021). These long-term negative behaviors and psychological outcomes can occur if individuals are involved in cyberbullying for long periods of time as victims or perpetrators. Technology use, such as mobile phone addiction, Internet addiction, etc., as personal factors, is directly related to cyberbullying behavior (Gül et al., 2018; Chung and Shin, 2020). The high level of bullying victims is significantly associated with high levels of problem internet use and mobile phone addiction (Li et al., 2020), while the victims develop higher levels of depression, perceived stress, loneliness, and social anxiety (Buelga et al., 2012; Iranzo et al., 2019; Bochkareva and Strenin, 2021), low self-concept, life satisfaction and emotional intelligence (Cañas et al., 2019), leading to suicidal tendencies, aggression, alcohol and drug abuse, truancy, and poor grades (Kang, 2015; Tözun, 2018). Therefore, the effect of mobile phone addiction on high school students may be achieved by increasing the risk of cyberbullying victimization.

Since there is no research to examine the combined effects of mobile phone addiction and high school students’ cyberbullying victimization on depression, the present study proposes hypothesis H2: Mobile phone addiction of high school students is positively correlated with cyberbullying victimization, which in turn is positively correlated with depression. In other words, high school students cyberbullying victims mediates the link between mobile phone addiction and depression.

Studies on Internet addiction have found that Internet addiction is associated with an increase in cyberbullying and bullying among adolescents (Floros et al., 2013), and that the rate of cyberbullying victimization and the prevalence of cyberbullying in the Internet addiction group are significantly higher than in the non-addiction group (Chang et al., 2015). On the one hand, individuals who spend more time on the Internet learn more about the use of technology, creating a power imbalance between bullying perpetrators and victims, then the cyberbullying intentions are more likely to develop into behavior (Shaikh et al., 2021); On the other hand, research shows that there is a positive correlation between internet usage time and cyberbullying perpetration (Tsimtsiou et al., 2017; Chacon-Borrego et al., 2018). Junior school students who spend more time playing games on weekdays are more likely to be involved in cyberbullying (HwaJin and Wanju, 2021), and younger students who are more active in using mobile phones are more likely to engage in cyberbullying than other students (Shin and Ahn, 2015). What’s more, there is a significant positive correlation between the factors in the Teen Smartphone Addiction Scale and the Cyberbullying Injury Scale (Chung and Shin, 2020). Teenagers who perpetrate cyberbullying score higher on depression, anxiety, negative self-esteem, physicalizing, and hostility (Calpbinici and Tas Arslan, 2019), so high school students with technology use problems such as mobile phone addiction are more likely to develop higher levels of cyberbullying, leading to depression (Yuan and Liu, 2021).

Since there is no research to confirm the positive predictive effect of cell phone addiction on cyberbullying among high school students, the present study proposes hypothesis H3: Mobile addiction of high school students is positively correlated with cyberbullying perpetration, which in turn is positively correlated with depression. In other words, high school students cyberbullying perpetration mediates the link between mobile phone addiction and depression.

Research shows that adolescent cyberbullying victimization is a predictor of cyberbullying (Álvarez-García et al., 2018; Ramos Salazar, 2021), while children who experience cyberbullying exhibit more aggressive behavior (Ijachi, 2019). GAM puts forward the path of how cyber victims can become cyber bullies. As a stressful event, cyber bullying experience consumes victims’ limited psychological resources. When completing tasks that require self-control such as experiencing the cyberbullying, the psychological resource will gradually be consumed, leading to a continuous decline in subsequent self-control performance (Baumeister et al., 2007), and more inclined to react impulsively, such as cyberbullying. In addition, the Frustration-Attack Hypothesis states that an individual’s frustration can lead to a “state of readiness” for attack behavior that can also be caused by other’s attacks and the habit of attack that has been developed (Berkowitz et al., 2016). Thus, victims of cyberbullying are more likely to develop an intention to bully others, that is, to gain dominance in a bullying way to show their strength, thereby feeling rejection from peers, and to have negative feelings such as depression in the bullying group (Reijntjes et al., 2010).

Since there is currently no direct evidence to support the positive projections of cyberbullying victimization on perpetration among high school students, the present study suggests hypothesis H4: Cyberbullying victimization is positively correlated with perpetration. Thus, the association between mobile phone addiction and depression is sequentially mediated by cyberbullying victimization and perpetration.

In online behavior, gender differences are an unavoidable topic. Related meta-analysis believes that gender is the influencing factor of cyberbullying (Kowalski et al., 2014), but whether it is a male or a female is more likely to become the perpetrator, the research has no consistent results (Beckman et al., 2013; Lapidot-Lefler and Dolev-Cohen, 2015). Most studies have found that women are more likely to become victims of cyber bullying than men (Moreno–Ruiz et al., 2019). Girls prefer to use social networking sites and use multiple social media tools at the same time, so they may be more vulnerable to cyberbullying victimization (Messias et al., 2014). In addition, girls with poor interpersonal relationships are more likely to suffer cyberbullying (Jing et al., 2009). However, surveys have shown that boys are more vulnerable to bullying (Erdur-Baker, 2010). After being victimized by cyberbullying, boys’ adventurous and impulsive personality traits may be more involved in violence and show higher levels of cyberbullying perpetration (Aricak, 2009; Gonzalez-Cabrera et al., 2019; Martinez-Pecino and Duran, 2019). Research suggests that boys are more prone to cyberbullying, which may be related to their personality and online preferences. Boys are more impulsive and like to play adversarial online games, while girls are more tolerant and less exposed to adversarial situations (Baldry et al., 2016; Tolga et al., 2017). Wright believes that the occurrence of cyberbullying has nothing to do with gender, and that those with masculine tendencies are relatively more likely to develop cyberbullying (Wright, 2017). At the same time, the personality characteristics of boys who are not good at expressing their emotions can also lead to the appearance of negative emotions such as depression (Pečjak and Pirc, 2017; Cañas et al., 2019). Therefore, boys who have suffered cyberbullying may be more likely to use the same method to conduct cyberbullying on others than girls.

However, due to the inconsistent conclusions on gender differences in cyberbullying behavior in existing studies, the above relationship still lacks empirical support. Based on this, the present study proposes Hypothesis H5: High school students have gender differences in cyberbullying behavior, and male groups are more likely to develop from cyberbullying victimization to cyberbullying perpetration, that is, there are gender differences in the sequential mediation of cyberbullying victimization and cyberbullying preparation between high school students’ mobile phone addiction and depression.

Cyberbullying victims caused by mobile phone addiction can cause depression in high school students, and victims tend to develop into cyberbullying practitioners, further increasing the level of depression. A multiple mediation model (Figure 1) was established for this study in order to test all possible mechanisms of these two mediators.

A cluster sampling method was adopted to select first-year students from two senior middle schools in a prefecture-level city in southwestern China as the survey participants in November 2021. Subject to the consent of the class teacher and the students themselves, postgraduates in psychology and psychiatry who had undergone uniform training would conduct group tests as a unit of the class. The participants were required to complete the questionnaire after reading the instruction carefully, and the test will take about 20 mins. A total of 1,400 questionnaires were distributed in the study. Questionnaires with the same answers or obvious answering patterns and more than 15% or more unanswered questions in any scale were excluded. Finally, 1,297 valid questionnaires were obtained, with an effective rate of 92.643%. Among them, boys accounted for 38.9% and girls accounted for 61.1%; urban students accounted for 25.7%, and rural students accounted for 74.3%; the average age was 15.50 years (SD = 0.573 years).

The questionnaire and methodology for this study was approved by the Human Research Ethics committee of the Kunming Medical University (Ethics approval number: 2021kmykdx6f66).

SPSS 22.0 software was used for descriptive statistics and correlation analysis. Mplus 8 software was used for structural equation models to establish multiple mediation model and multi-group analysis to test gender differences, and Bootstrap method of repeated sampling 1,000 times was used to test the mediation effect and estimate the confidence interval (Mackinnon et al., 2004). With respect to goodness of fit, two classes of indexes (i.e., statistical indicators reflecting the degree of fit between the hypothesized conceptual model and the empirical data) were adopted: Absolute and Relative Goodness-of-Fit Indices. The former included χ²/df, the root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) (Schafer and Graham, 2002; Hair et al., 2011). The latter comprised comparative fit index (CFI) and Tucker-Lewis Coefficient (TLI). Thresholds for good model fit were: χ²/df > 3.0, RMSEA < 0.08, SRMR < 0.08, CFI > 0.90, TLI > 0.90 (Marsh and Hau, 1996; Schermelleh-Engel et al., 2003). Statistical significance test level α = 0.05, *p < 0.05, ^**p < 0.01, ^***p < 0.001.

Since the questionnaires required for the study all require the participants to self-report, in order to control possible Common method biases, this study takes the following measures for process control: (1) Conduct the test collectively, read out the instructions uniformly, and emphasize that the research is only used for scientific research, and all information is absolutely confidential; (2) Emphasize that there is no right or wrong answer, and the participants only need to choose the one that they agree with or is relatively most suitable option for them and set up reverse scoring questions; (3) All questionnaires are distributed and withdrawn on the spot. In addition, statistical tests are performed using Harman’s One-factor Test. It was found that there were 11 eigenvalues greater than 1, which explained 55.297% of the variation, and that the first factor explained only 18.609% of the variance. Therefore, there is no serious common method deviation problem. At the same time, the control for effects of the unmeasured latent methods factor was used to test whether there was a serious common method biases in this study (Xiong et al., 2013). After adding the common method factor, the model fitting index is higher (RMSEA = 0.043, SRMR = 0.034, CFI = 0.982) than that without adding the common method factor (RMSEA = 0.070, SRMR = 0.045, CFI = 0.938), but they are all less than 0.05. It also shows that there is no serious common method biases problem.

Correlation analysis shows that the correlation coefficient of the total score of each variable is between 0.095 and 0.447 (all p < 0.01). The differential test showed that the level of depression and mobile phone addiction was significantly higher in girls than in boys, and that the level of cyberbullying victimization and cyberbullying perpetration in boys was significantly higher than that in girls. See Table 1.

Test the measurement model before modeling (Wen and Ye, 2014), and build a structural equation model based on theoretical assumptions. In the present study, due to the complexity of the model and many estimated parameters, the item parceling strategies was used to simplify the model in the subsequent analysis. The two variables of cyberbullying victimization and preparation both satisfied the unidimensional and homogeneous conditions. The shortening method in the factorial algorithm was used to pack each variable into three indicators (Yan and ZhongLin, 2011). Mobile phone addiction included six dimensions: withdrawal behavior, salience behavior, social comfort, negative effects, app use of App, and app update renewal of App; and depression included depressed affect, positive affect, somatic and retarded activity and interpersonal four dimensions, which were multidimensional scales. Moreover, the internal-consistency approach was adopted for parceling, that is, the topics under the same dimension were packaged. This could reduce the differences within the group and increase the consistency of the indicators.

The measurement model includes four latent variables of mobile phone addiction and depression, and 16 observation variables. Taking mobile phone addiction as an independent variable, depression as a dependent variable, cyberbullying victimization and cyberbullying perpetration as mediating variable, using structural equation model for path analysis, maximum likelihood method for parameter estimation, Bootstrap method (sample times is 1,000) to conduct a mediation effect test. The model is shown in Figure 2. Fit analysis showed that the data fit the model well: χ²/df = 4.984, RMSEA = 0.055, SRMR = 0.037, TLI = 0.938, CFI = 0.949. Mobile phone addiction significantly positively relates depression (β = 0.376, p < 0.001) and cyberbullying victimization (β = 0.222, p < 0.001), and has no significant relation on the cyberbullying perpetration (β = −0.033, p > 0.05). The cyberbullying victimization (β = 0.294, p < 0.001) and the cyberbullying perpetration (β = −0.120, p < 0.05) have a significant relation on depression, and the cyberbullying victimization has a significant negative relation on the cyberbullying perpetration (β = 0.604, p < 0.001).

A further examination of the mediation effect (Table 2) showed that the Bootstrap 95% confidence interval of the total indirect effect of cyberbullying victimization and cyberbullying perpetration between mobile phone addiction and depression was not included the value of 0. It shows that there are significant mediating effects in the effect of mobile phone addiction on depression. This mediating effect consists of three paths: First, the path (Indirect1) consisting of mobile phone addiction → cyberbullying victimization → depression. The confidence interval of its indirect effect does not contain a value of 0, indicating that the indirect effect produced by this path is significant (effect value is 0.068); second, the path (Indirect2) consisting of mobile phone addiction → cyberbullying perpetration → depression. The confidence interval of its indirect effect contains a value of 0, indicating that the indirect effect of this path has not reached a significant level; third, the path (Indirect3) consisting of mobile phone addiction → cyberbullying victimization → cyberbullying perpetration → depression. The confidence interval of its indirect effect does not contain a value of 0, indicating that the indirect effect produced by this path is significant (effect value is −0.027).

In order to test whether the mediation model had cross-group stability, the present study conducted a multi-group path analysis on the gender difference of the model. The results showed that the model had statistically significant differences in gender (χ²/df = 2.297, p < 0.001); further analysis found that gender played a role in the path from cyberbullying victimization to cyberbullying perpetration. The path coefficients of the boys and girls from cyberbullying victimization to cyberbullying perpetration were 0.856 (p < 0.001) and 0.387 (p < 0.05), respectively, indicating that gender had a moderating effect on the path from cyberbullying victimization to cyberbullying perpetration. Compared with girls, boys were more likely to develop cyberbullying behaviors after experiencing cyberbullying.

The results of this study show that H1 is established, high school students have positive predictive effects on depression, and high school students with more severe mobile phone addiction have higher levels of depression. Consistent with existing research findings (Çağan et al., 2014; Xiaodan et al., 2016; Alhassan et al., 2018), mobile phone addiction can lead to depression, anxiety, and sleep problems (Thomee, 2018), and teenagers who spend longer on mobile social media score higher on the depression scale on average (Calpbinici and Tas Arslan, 2019). It supports the research on mobile phone addiction and other psychosocial adaptation problems, and shows that mobile phone addiction is an important factor affecting the level of psychosocial adaptation of high school students. Mobile phone addiction can easily cause students to avoid learning tasks that need to be completed, reduce the efficiency of task completion, and have a negative impact on the quality of individual sleep (Thomee, 2018), accompanied by other negative symptoms such as high levels of anxiety, depression, negative self-esteem, physicalizing, and hostility (Bozzola et al., 2019; Calpbinici and Tas Arslan, 2019). Therefore, educators should pay attention to the depression of high school students addicted to mobile phones, carry out psychological guidance in a timely manner, and alleviate depression by guiding students to use mobile phones rationally.

The results of this study confirm the H2, that high school students’ mobile phone addiction positively predicts cyberbullying victimization, and cyberbullying victimization positively predicts depression; that is, cyberbullying victimization plays a mediating role between high school students’ mobile phone addiction and depression. Consistent with existing research, there is a positive correlation between cyberbullying and internet addiction (Tsimtsiou et al., 2017; Imek et al., 2019; Wachs et al., 2020). The findings support the General Aggressive Model of cyberbullying, in which mobile phone addiction, as a characteristic of personal technology use, will increase the possibility of being a victim of cyberbullying in high schools (Kowalski et al., 2014). On the one hand, as high school students who are addicted to mobile phones are more exposed to virtual Internet environments and thus receive more hostile messages on the Web (Alvarez-Garcia et al., 2015; HwaJin and Wanju, 2021). On the other hand, mobile phone-addicted teens exhibit higher levels of emotional problems and social dysfunction problems (Jain and Agrawal, 2020; Sheinov, 2021), is more likely to be targeted for cyber violence. Moreover, the anonymity of cyberbullying increases the negative impact of bullying victims by preventing them from judging the source of bullying, and non-face-to-face communication prevents victims from venting the negative emotions generated by bullying, thereby increasing their level of depression. Therefore, for high school students with high level of mobile phone addiction, educators should pay attention to the risk of their cyberbullying victimization, on the one hand, provide crisis intervention counseling for the victimization of cyberbullying, to prevent depression caused by the cyberbullying, and on the other hand, from the perspective of taking measures to alleviate mobile phone addiction to reduce the possibility of cyberbullying high school students.

This study shows that the cyberbullying perpetration negatively predicts depression in high school students, while mobile phone addiction does not have a significant predictive effect on the cyberbullying perpetration, that is, the mediating effect of cyberbullying perpetration between mobile phone addiction and depression in high school students is not significant. What’s more, the cyberbullying victimization significantly positively predicts the cyberbullying perpetration, that is, the cyberbullying victimization and the cyberbullying perpetration have a sequential mediating effect between mobile phone addiction and depression in high school students. The conclusions of this study support the significant correlation between problematic mobile phone use and the cyberbullying victimization and the cyberbullying perpetration (Tsimtsiou et al., 2017; Gül et al., 2018), and there is a significantly positive correlation relationship between the cyberbullying victimization and the cyberbullying perpetration (Cañas et al., 2019). Contrary to the conclusions of existing research, this study shows that mobile phone addiction of high school students cannot directly increase their cyberbullying perpetration, but the victimization of cyberbullying caused by mobile phone addiction can trigger high school students to perpetrate cyberbullying behaviors and relieve their depression levels. The transition from victimized behavior to bullying behavior supports the Frustration-Attack Hypothesis that external attacks will cause the individual to suffer frustration and form a willingness to attack, and the cyberbullying information experienced serves as a weapon to initiate information. In addition, according to GAM, cyberbullying victims have severely depleted self-control resources and are more likely to make impulsive behaviors. Therefore, their willingness to bully can easily turn into aggressive behavior (Berkowitz et al., 2016). At the same time, the victim’s bullying of others has played a role in venting negative emotions to a certain extent, and due to the disinhibition effect brought by the anonymity of cyberbullying, the victim has a small exposure risk and psychological burden for harming others, thus showing more aggressiveness, and alleviating depression caused by self-victimization through cyberbullying of others.

The research results confirmed Hypothesis H4, that is, compared with female high school students, boys are more likely to develop from cyberbullying victimization to cyberbullying perpetration. Consistent with existing research, boys’ cyberbullying victimization, and cyberbullying perpetration levels are higher than those of girls (Aricak, 2009; Erdur-Baker, 2010; Gonzalez-Cabrera et al., 2019; Martinez-Pecino and Duran, 2019), who is more likely to be attacked by cyberbullying. Although gender issues have always been controversial in previous research on cyberbullying (Barlett and Coyne, 2014), the results of this study support the claim that boys are more likely to be perpetrators of cyberbullying. This may be because boys are more likely to have the background and conditions for cyberbullying because they have access to the Internet and spend more time on the Internet every day (Zhehui et al., 2010). In addition, boys usually have a low level of rumination, a high level of moral excuse and a low sense of guilt. After being bullied, they tend to use distorted consequences and victim attribution to defend their unethical behavior, implement cyberbullying to retaliate against others and alleviate the impact of ruminating emotions (Cancan et al., 2019). Moreover, studies have shown that boys have a higher degree of moral disengagement than girls (Dongling and Meifang, 2013), and the use of moral disengagement strategies allows young people to cognitively moralize their unethical behaviors, so that boys are more likely to implement them without scruples. Cyberbullying (Wang et al., 2016). Other studies have shown that girls are better than boys in empathy (Jolliffe and Farrington, 2006), and individuals with low empathy are more likely to engage in cyberbullying (Kokkinos and Kipritsi, 2017), which also shows that boys are more likely to be cyberbullies than girls.

The findings of the present study explain the mechanism of transition of cyberbullying victims to bullying perpetrators, and although cyberbullying plays a role in alleviating negative emotions for individual victims, the end result is to promote the spread of cyberbullying, thus affecting the health and harmony of the entire network ecology. Therefore, in order to prevent the spread of cyberbullying behavior on the Internet, on the one hand, it is necessary to start from the source and use various interventions to alleviate the level of mobile phone addiction among high school students, thereby reducing the risk of cyberbullying victims and alleviating depression, and on the other hand, it is the key to prevent high school students from turning victims of cyberbullying into perpetrators and to build a healthy network by improving their self-control ability, coping with bullying experiences reasonably, and correctly diverting negative emotions.