How many cyberbullying(s)? A non-unitary perspective for offensive online behaviours.

Research has usually considered cyberbullying as a unitary phenomenon. Thus, it has been neglected to explore whether the specific online aggressive behaviours relate differentially to demographic features of the perpetrators of online aggressive actions, their personality characteristics, or to the ways in which they interact with the Internet. To bridge this gap, a study was conducted through a questionnaire administered online to 1228 Italian high-school students (Female: 61.1%; 14-15 yo: 48.%; 16-17 yo: 29.1%; 18-20 yo: 20.4%, 21-25 yo: 1.6%; Northern Italy: 4.1%; Central Italy: 59.2%; Southern Italy: 36.4%). The questionnaire, in addition to items about the use of social media, mechanisms of Moral Disengagement and personality characteristics of the participants in the study, also included a scale for the measurement of cyberbullying through the reference to six aggressive behaviours. The results indicate that cyberbullying can be considered as a non-unitary phenomenon in which the different aggressive behaviours can be related to different individual characteristics such as gender, personality traits and the different ways of interacting with social media. Moreover, the existence of two components of cyberbullying has been highlighted, one related to virtual offensive actions directly aimed at a victim, the other to indirect actions, more likely conducted involving bystanders. These findings open important perspectives for understanding, preventing, and mitigating cyberbullying among adolescents.

Introduction

Social media and the Internet are today the prevailing means of communication among adolescents and young adults. While online communication media provide many opportunities, their use also exposes individuals to the risk of being victims of aggressive behaviours and misconduct. A single action such as posting an embarrassing photo or video of someone on a social network is enough for this to be seen by many people many times [1-4], with potential negative consequences for the victim, such as low self-esteem, distress, depression, loneliness, sadness [5-8]. Such negative consequences may be even worrisome. A meta-analysis of 47 studies found that bullying victimization and bullying perpetration may be associated with suicidal ideation and behaviour [9].

A very recent review on cyberbullying among adolescents and children, based on 63 references [10], reported that the prevalence of cyberbullying perpetration varied across studies from 6.0% to 46.3%, while the rates of cyberbullying victimization from 14.0% to 57.5%. Other authors [3, 11–13] underline that about 20–40% of adolescents are harassed online. These numbers have been constantly on the rise [14, 15], and the number of studies aimed at analysing cyberbullying has increased as well. In a recent analysis, it was highlighted that since 2011/2012 there has been a linear increase in scientific articles on cyberbullying each year, reaching 268 publications in 2019 [15].

In this regard, several reviews have already been produced [3, 4, 16–18] that showed how research has essentially focused on some fundamental aspects related to cyberbullying, and that there are still many open issues.

First, we can note some difficulties in the definition of the phenomenon [19], though some common characteristics of cyberbullying behaviours have been highlighted so far. In generally accepted definitions, cyberbullying has several key characteristics: it is a behaviour intended to hurt, it is enacted by one or more people, it is performed through an electronic medium, and it is directed at those who cannot easily defend themselves [2]. According to [20], cyberbullying should be distinguished from cyber incivility and cyber aggression. The former one, in fact, does not involve an imbalance of power, and should refer to any rude or rough behaviour carried out through electronic means. Cyber aggression, on the other hand, refers to those behaviours aimed at hurting, causing injury, and like cyber victimization does not involve an imbalance of power [21]. Cyberbullying thus appears to be based essentially on an abuse of power [22, 23] that is manifested by behaviours that are systematically aimed at causing harm and that are carried out using electronic means [2].

However, definitions can be different depending on one’s point of view, and acting like a bully can mean different things to different people. For example, in a study conducted on a UK sample, O’Brien [24] showed that teachers tend to qualify as bullying a higher number of behaviours, on the basis of factors such as the way the behaviour affects the victim, gender discrimination, besides power relations between the aggressor and the victim.

Above all, there are two aspects in the attempts to define the peculiarities of cyberbullying that are rather opaque, as pointed out by Slonje et al. [4]. The first has to do with the issue of repetition of behaviour. The Internet, in fact, can be a powerful amplifier and booster of any word or action. And so even the one-time offense can become "endless." The other aspect has to do with determining what might contribute to an imbalance of power between aggressor and victim. As argued by Vandebosch & van Cleemput [25], this may also relate to greater technological experience or to the ability to behave anonymously. Moreover, a meta-analysis of 22 studies, has shown that often the role of the victim and the role of the bully can involve the same person, and the occurrence of this condition may depend on the culture of reference, i.e. Central European, Mediterranean, North American, South American and Asian cultures [26].

It seems interesting to highlight that, recently, several studies have been trying to refine algorithms with the aim of detecting offensive behaviour in different social media [27-29]. These studies are often based on the results provided by the psychological analysis of recent years, thus confirming the role of personality factors [28, 30]. They also contribute to better define what cyberbullying is and detect it more readily, for instance by capturing repetitive behaviour and sentiment information [31, 32]. To date, however, these studies are often based on datasets collected from a single or few social media [28, 30, 32, 33], and they do not always consider all the different offensive behaviours, such as those concerning ethnicity, religion, physical and cognitive impairments [30].

Because of these blurry aspects, it seems really difficult to clearly qualify what cyberbullying is and to distinguish it from other forms of virtual aggressions.

Variables associated with cyberbullying

Even when we consider the subjective variables, the results are not always homogeneous. Above all, with respect to gender and age, the studies do not show consistent data, although the trend seems to indicate a greater harassment towards girls, with a peak for bullying behaviour between the ages of 12 and 15 [2, 34–36] (for a recent review of studies on the subject see [10]). Tokunaga [3] suggested a curvilinear trend with a peak that can be identified between 7th and 8th grades, while Barlett & Coyne [37], by directly analysing the relationships between gender and age, showed an interesting relationship between these two variables. Generally, it is boys who are more involved in offending behaviours. However, this difference between the sexes in the likelihood to engage in offensive behaviour seems to become evident only as boys get older.

Another key factor for predicting aggressive behaviour on the net is given by the time spent on it, which, if excessive, is often associated with psychological distress. A meta-analysis showed a correlation between a problematic use of the Internet and depression, though this effect is essentially associated with male gender [38] Moreover, some scholars found a correlation among aggressive virtual behaviour, a greater experience in the use of communication technologies and time spent online [39-42]. At the same time when social media—the communication means that are most involved in acts of cyberbullying—are taken into account, they do not seem to have a direct link to misconduct; though the use of social media that most easily allow behaviours in anonymity seems more connected to the occurrence of offensive behaviours [13]. However, intensive use of social media seems more clearly to be a risk factor for the victim more than for the perpetrator [39].

Several studies have also focused on the personality characteristics of bullies (see, for instance [43]). Among the most investigated aspects are those related to personality traits in reference to the theory of the big five [44]. In an analysis aimed at distinguishing bullying from cyberbullying [45], it was found that, unlike so-called traditional bullying, cyberbullying is essentially negatively related to the trait of agreeableness. This finding has emerged also in other studies in which, however, cyberbullying was also found to be negatively related to conscientiousness [42] and to affective empathy [46]. Recently, a study attempted to relate the personality characteristics of Twitter users to their offensive messages [47]. Using an automated categorization of both personality characteristics and user behaviours, it was highlighted that extraversion, agreeableness, and neuroticism are highly relevant predictors (up to 96% accurate) for identifying those Twitter users who can be qualified as bullies.

More specifically, personality characteristics have been related to Moral Disengagement, that is those thought mechanisms that are exercised to diminish the sanctioning aspects of moral principles [48-50]. In essence, moral disengagement mechanisms are justifications for one’s thoughts or actions that are made acceptable by referring to moral principles that are considered at a higher level, such as when sacrificing an individual for the collective interest or punishing those who are thought to deserve punishment. It has been previously reported that subjects who self-report having engaged in cyberbullying differ from those who have never done so for a greater reliance on all moral disengagement mechanisms [13]. This finding had already emerged in a previous study by Meter & Bauman [51], who found that moral disengagement is related as much to cyberbullying as it is to traditional bullying. Again, however, the relationships between moral disengagement and cyberbullying are not fully defined. The use of moral disengagement mechanisms in the context of online relationships can be particularly variable, and thus quite different from those involved in bullying perpetrated in non-virtual contexts. In fact, while traditional bullying seems to be context independent, the same appears not to be true for cyberbullying [52]. From the results of the analysis conducted by Paciello and colleagues [52], the possibility emerges that online moral disengagement should be formulated and studied as a theoretical construct separate from the one which manifests in traditional contexts, although the two constructs stand out as related.

Measuring cyberbullying

Another issue related to the understanding, definition and prevention of cyberbullying refers directly to the instruments commonly used to measure its occurrence. According to many studies the different manifestations of bullying, such as cyberbullying, are better explained as indicators of a unidimensional construct [53-56]. In fact, the many instruments used and proposed in the literature generally involve the use of a variable number of items that, with ordinal, interval, or ratio response scales [57] aim to uniquely qualify the experience of cyberbullying. In 2014, an analysis conducted to evaluate self-report instruments in reference to cyberbullying [58] showed that many scales were already available. More precisely, 27 scales were identified, although not all of them at an adequate level of development and with satisfactory validity and reliability indices. Today it can be said that there are scales with a long tradition such as the revised version of the Olweus Bully-Victim Questionnaire [59], the Peer Relations Questionnaire [60], and the Forms of Bullying Scale [55]. And it was from these three scales that Thomas and colleagues [57] validated a scale that allowed them to assert that the construct of cyberbullying is essentially one-dimensional (see also [61]). Despite this result, however, assumptions can be made in relation to the fact that the results obtained are evidently determined by the behaviours included in the scale which may be more or less similar to each other.

In a recent review of tools used for measuring cyberbullying Chun et al. [62] highlighted that of the 64 studies considered, only 15 followed a correct approach to scale construction with respect to item selection. Furthermore, only 1 of these had subscales to assess perpetration-only cyberbullying. This is the 19 items scale proposed by Álvarez-García et al. [34] inspired directly by the multifactorial model proposed by Palladino et al. [63]. In these and a few other cases (see [64]), an attempt was made to structure scales that would account for the different behavioural facets of cyberbullying through an appropriate number of items. Factor analyses conducted on the data identified some factors (two to four) that could provide a finer understanding of the phenomenon. These studies, however, have not attempted to account for both the various subjective (e.g. personality factors) or contextual factors (e.g., the possibility to have frequent interactions on the Internet) that are likely to be involved in motivating the different manifestations of online aggressive behaviours.

Other scales, such as the one used by Meter & Bauman [51] were created with an intent that clearly aims at brevity. This tool presents only 6 items for behaviours that describe typical cyberbullying actions by different means, which are paralleled with as many items on cyber-victimization. Specifically, those misconducts have to do with verbal aggressiveness, sending nasty messages, revealing secrets, spreading gossip, visual aggressiveness, and impersonation. Usually these items are analysed in a unified manner, for example as the authors did in reference to moral disengagement [51]. However, an analysis that attempts to relate the different behaviours to different subject characteristics could be informative at a more fine-grained level. Therefore, it seems reasonable to try to analyse whether the different cyberbullying behaviours described within the same scale could be predicted by different subjective and contextual-behavioural variables. This could lead to a more clearly referable understanding of the phenomenon in its different manifestations.

The study

The aim of the study was to investigate different forms of cyberbullying involving the use of social media and the internet by adolescents, to understand their determinants with respect to individual psychological variables and to explore associations with demographic variables and variables related to the pattern of social networks use.

Four different research questions (RQ) were addressed in the study:

RQ1. Are there differences in the frequency of different types of cyberbullying behaviours, overall and as a function of socio-demographic variables? We might expect that different forms of aggressive behaviour have different frequencies (H1) while it is not clear whether differences in cyberbullying frequency across categories such as gender (i.e. more frequently reported by boys) or age should be found [37] for all the different behaviours (H2) or not.

RQ2. Are different types of cyberbullying behaviours differently related to individual features such as personality traits and moral disengagement? It is known from several studies [13, 51, 52] that cyberbullying is more common in adolescents who exhibit certain characteristics such as a high level of moral disengagement, and some personality characteristics as neuroticism. Escortell and colleagues [65] found a relationship among online aggressors and a minor level of agreeableness and conscientiousness and major level of neuroticism, while it has been observed that agreeableness and openness to experience are [66, 67] a key factor to be a victim. Although there is no general consensus among the findings on this issue, we can suppose a relationship between specific forms of aggressive behaviour and personality, cognitive and behavioural factors (H3).

RQ3. Are different types of cyberbullying behaviours specifically related to different profiles of internet and social media usage? Previous research has shown that aggressive behaviour on the internet is related to the time spent online and using social media [68], but it is not clear whether this association can be found for cyberbullying in general or whether it is specific to certain forms of misconduct only (H4). The latter assumption can be made if we take a multidimensional perspective of cyberbullying. Moreover, with few exceptions [13], factors such as the number of accounts or followers on social networks have not been adequately studied in relation to cyberbullying.

RQ4. Is cyberbullying better conceived and measured as a unitary construct or as a multidimensional construct? It is possible that inconsistencies among findings about the association of aggressive behaviours online and demographic or individual variables might be due to differences in the measures of cyberbullying used in the studies. We hypothesise that even investigating a small number of behaviours, as in the case of Meter & Bauman’s scale [51], non-unified perspectives of cyberbullying may emerge (H5).

To address these research questions, and test the various hypotheses described above, we structured a questionnaire that was administered to a large sample of high school students.

Materials and methods

Participants and procedure

One thousand two hundred and twenty-eight (1228) students from high-schools, technical and professional schools, participated in the study. The majority were girls (61.1%), and half of the participants were in the 14–15 age range (48.4%), followed by the 16–17 age range (28.7%) and by 18–20 years (20.2%). Only 19 students belonged to the 21–25 age range (1.5%). Demographic information about participants in the sample are presented in Table 1.

Table 1

Socio-demographic characteristics of the sample.

Variable	N	%
Gender	NA = 2
Female	750	61.2%
Male	476	38.8%
Age	NA = 14
14–15 years	594	48.9%
16–17 years	353	29.1%
18–20 years	248	20.4%
21–25 years	19	1.6%
Geographical region a
Northern Italy	50	4.1%
Central Italy	723	58.9%
Southern Italy and Islands	445	36.2%

Descriptive statistics (frequencies) about the socio-demographic characteristics of the sample (N = 1.228).

aRegions in Northern Italy: Trentino Alto-Adige, Lombardia, Valle d’Aosta, Piemonte; Regions in Central Italy: Tuscany, Lazio; Regions in Southern Italy: Campania, Sicilia.

The schools were distributed across the Italian country (Northern Italy: 4.1%; Central Italy: 59.2%; Southern Italy: 36.4%) and were chosen via personal contacts of a small group of university students who served as research assistants in the study and that attended those schools before enrolling to college. The headmasters of the schools were contacted and informed about the study. The parents of minors were also contacted through the school and asked to give consent to the participation of their child to the study. After having received their authorization, students were asked to complete a self-reported on-line questionnaire on a voluntary basis. The ethical aspects of the study were approved by the department, acting as Ethical Committee, in March 2019 (report n. 10/2019 of 13 March 2019). The different schools involved were all contacted at different times during 2019, and data collection ended in December 2019.

Materials and measures

The questionnaire included 32 questions, and was structured into 5 sections, dedicated to collect, respectively, socio-demographic information about participants, information on their patterns of social media use, their personality traits [69], the levels of moral disengagement about cyberbullying [51] and the frequency of cyberbullying behaviours [51].

In the first section participants were asked to report their gender, their age, the city in which they lived, and the type of secondary school they were attending.

The use of social networks was investigated in the second section through multiple choice questions on the most frequently used social media (Youtube, Instagram, Snapchat, Google+, Whatsapp, Twitter, Facebook, etc), and the time spent online every day (“never”; “less than 1 hour”; “1 to 3 hours”; “more than 3 hours”). Feelings related to network use—such as jitters if unable to use the Internet were also inquired (“If I can’t use Internet I feel nervous”) were measured on a five-point agreement scale (1 = strongly disagree / 5 = strongly agree). Also the number of social profiles were investigated (“None”; “One”; “More than one”) as well as the number of followers of each participant (“Less than 200 followers”, “More than 200 to 500”, “More than 500 to 1000”, “More than 1000 to 5000”, “More than 5000 followers”).

The last three sections of the questionnaire, as detailed in the following paragraphs, included the Italian version of three scales aimed at measuring personality traits [70], moral disengagement about cyberbullying and cyberbullying behaviours [50, 51] respectively.

Big Five Inventory (BFI)

Personality was measured using the Italian version of the Big Five Inventory-10 [69, 70], a very brief scale comprising only 10 items, which measures the five personality traits from the Big Five model: Agreeableness, Extraversion, Conscientiousness, Emotional Stability, and Openness to Experience. Responses for all the items are expressed on a 5-point agreement scale (1 = strongly disagree and 5 = strongly agree), and each trait is measured by two items, with one item for each scale reverse coded. Despite its brevity, this instrument has shown good psychometrics properties in the validation study [70].

Moral disengagement about cyberbullying

Moral Disengagement about cyberbullying was measured using the Italian version of a self-report scale proposed by Meter and Bauman [51] and including eight items. Each item refers to a different type of cyberbullying behaviour, for example “Cyberbullying annoying classmates is just teaching them a lesson” or “It’s okay to treat someone badly if they behave like a jerk”, and responses are expressed on a 5-point agreement scale, where 1 corresponds to “strongly disagree” and 5 to “strongly agree”. The scores are then averaged across items to get a summary measure of the level of moral disengagement. The items had been translated in Italian by the authors for a previous study [68] in which a confirmatory factor analysis had shown excellent fit and prediction validity.

Cyberbullying

Cyberbullying perpetration was measured using the Italian version of a self-report scale devised by Meter and Bauman [51], which includes 6 items, each referring to a different type of online aggressive behaviour directed against someone: 1) “Sending a mean or nasty text message”2) “Sending mean or nasty email messages”; 3) “Sending an embarrassing photo of someone via cell phone”, 4) “Pretended to be someone else on the Internet”; 5) “Revealing someone else’s secrets online or by cell phone without their permission”; 6) “Spreading a rumour about someone on the Internet”. For each behaviour, participants were asked to report how often they had carried it out, on a 4-point frequency scale ranging from never to more than five times (1 = never; 2 = 1–2 times; 3 = 3–5 times; 4 = 5+ times). The items had been translated in Italian by the authors for a previous study [68] in which a confirmatory factor analysis had shown excellent fit.

The full questionnaire used in the study is available in S1 Text.

Statistical analysis

Descriptive statistics (means and standard deviations for numeric variables, and frequency tables for categorical ones) were computed to characterise the composition of the sample with respect to socio-demographic features, personality characteristics of respondents, social network use, moral disengagement about cyberbullying and perpetration of cyberbullying acts. Chi-squared tests were used to compare frequency distributions across gender and age, and t-tests to compare the means of numerical variables across genders.

Pearson product-moment correlations between personality factors, aggressive behaviours and moral disengagement about cyberbullying were computed to understand the possible relationships between these variables. Holm’s method was used to adjust p-values for multiple comparisons.

Ordinal logistic regression models were used to investigate predictors of the self-reported frequency of the different cyberbullying behaviours, measured on an ordinal scale with 4 levels (“never”, “1 to 2 times”, “3 to 5 times”, “more than 5 times”). For each behaviour, a separate regression model was used, using ratings of the self-reported frequency of perpetration as dependent variable. Predictors included in all the models were: age, gender, the five personality factors, the negative feelings when they do not have access to the internet, the time spent on social media, the number of social profiles and moral disengagement scores. All the numerical predictors were centred on the mean. For ordinal and nominal predictors were used treatment coding, and the reference categories for the categorical variables were chosen in the following way. Gender was coded so that it was 0 for “female”, and 1 for “male”. For age, the smaller age class was used as the reference category. For the number of followers and the time on social networks, which both had 3 levels, the middle category was chosen as the reference (“200–500 followers”, “1–3 h/d on social networks”), while the number of social network accounts was coded as 0 for those with only one account, and 1 for those with more than one account. Internet addiction, which was measured on a 5-point scale, was treated as a numerical variable, and centred on the mean. All the predictors were entered simultaneously in the regression models. Participants who had reported to never use social networks, were excluded from the regression analyses.

To analyse the factorial structure of the measurement of cyberbullying we have used a combination of Exploratory and Confirmatory Factor Analysis, conducted respectively on 30% (train set) and 70% (test set) of the data (randomly split). EFA was conducted using minimal residual for factor extraction and oblimin rotation. Polychoric correlations were used for the analysis, and parallel analysis was used to determine the number of factors to retain. In the CFA we tested two models, the one extracted by the EFA and a single factor model. The indicators were left on the categorical, ordered, response scale, and diagonally weighted least squares (DWLS) was used to estimate the model parameters. The fitness of the models was compared using several fit indexes (Comparative Fit Index—CFI; Normed Fit Index—NFI; Non-Normed Fit Index—NNFI; Root Mean Square Error Approximation—RMSEA).

The measurement model that had better fit in the CFA was used in a structural equation modelling analysis (SEM). The effects of personality traits (measured by the Italian BFI-10 scores) on the two cyberbullying factors (latent dependent variables), direct and mediated by moral disengagement (latent mediator variable) were tested in the model, along with the direct effect of a measure on an ordinal scale of internet addiction. A multigroup SEM analysis was conducted for each of these grouping factors: gender, time on social networks and having (vs not having) more than one profile on social networks. Differences in the means of the latent variables across groups in each category were tested after assessing measurement invariance (configural, weak and strong), using the following analytical strategy. For each grouping factor, first we conducted a multigroup SEM analysis allowing all the parameters in the model to vary freely across groups (i.e. parameters were estimated separately for each group), and assessed the fit of the multigroup model using fit indexes to verify whether configural invariance held (good fit) or not. Weak and strong configural invariance were then tested fitting a second and a third multigroup model in which the loadings of the latent factors (step 2) and the intercept of the latent variables’ indicators (step 3) were constrained to be equal across groups, allowing all the other parameters to vary, and comparing the significance of change in χ. A non-significant change was taken as evidence that the more restrictive model (higher invariance) fitted data as well as the less restrictive model, and therefore should be preferred (being more parsimonious) [71].

Personality traits were included as observed variables (aggregated scores of the two items in each BFI trait), as indicated for this brief scale Moderation of the effects of personality traits and moral disengagement by each of the grouping factors was also assessed in the multigroup analysis, by estimating parameters for the change in the slope of each effect across groups.

All the analyses were conducted using R version 4.0.2 [72], using the functions in the package lavaan (version 0.6–9) [73]. All the code and data used in the analysis are available online in a compressed folder (S2 File). In Fig 1 a visual summary of the methodology is presented.

Fig 1

Visual summary of the methodology.

RQ1. Are there differences in the frequency of different types of cyberbullying behaviours, overall and as a function of socio-demographic variables?

Results

Cyberbullying perpetration

We computed the percentages of the responses concerning the self-reported frequency of perpetration of the six different cyberbullying behaviours included in the questionnaire. The percentages are plotted in Fig 2 as stacked bar charts. For each behaviour, most participants reported they had never perpetrated it, and only a minority (from 2% to 18%), reported having it done three or more times. A significant Chi-square test showed that the distribution of self-reported frequencies varied across cyberbullying behaviours (χ(15) = 631.2, p < .001). The lowest frequency of perpetration was found for sending mean or indecent emails (never enacted by 94.1% of participants), and the highest for sending embarrassing photos (more than once by 48.1% of participants).

Fig 2

Self-reported frequency of cyberbullying.

Stacked frequencies bar charts showing the distribution of responses about the self-reported frequency of different types of cyberbullying behaviours.

The distribution of the self-reported cyberbullying perpetration frequency varied significantly across genders for three behaviours: sending indecent messages (χ(3) = 40.67, p < .001), sending indecent emails (χ(3) = 8.92, p = .028), and sending embarrassing photos (χ(3) = 10.06, p = .018). Girls were more likely to have never sent indecent messages (63.9%) than boys (48.2%), and less likely to have sent them more than five times (girls: 5.6%, boys: 13.1%). Girls were also more likely to have never sent indecent emails (95.5%) or embarrassing photos (58.6%) than boys (never sent indecent emails: 92.0%, never sent embarrassing photos: 54.9%).

RQ2. Are different types of cyberbullying behaviours differently related to individual features such as personality traits and moral disengagement?

Personality and moral disengagement

In Table 2 are reported the average scores for the measures of personality traits and moral disengagement in the sample, overall and by gender. A series of independent sample t-tests showed that girls were significantly less extravert and agreeable, they had less recourse to moral disengagement mechanisms and were less emotionally stable and open than boys. The average score on the question about feeling nervous without access to the internet is also reported in the bottom row (M = 2.69, SD = 1.18), and for this variable no significant differences were found by gender and age group.

Table 2

Descriptive statistics about personality, moral disengagement and internet addiction.

	All participants	Females	Males	p-value
	N = 1228	N = 750	N = 476
Extraversion	3.35 (0.93)	3.25 (0.93)	3.50 (0.90)	<0.001
Agreeableness	3.10 (0.87)	3.03 (0.86)	3.22 (0.86)	<0.001
Conscientiousness	3.27 (0.92)	3.26 (0.92)	3.30 (0.92)	0.402
Emotional Stability	2.73 (1.06)	2.49 (1.04)	3.11 (0.98)	<0.001
Openness	3.35 (1.00)	3.40 (0.97)	3.26 (1.03)	0.024
Moral Disengagement	1.77 (0.63)	1.64 (0.54)	1.96 (0.71)	<0.001
Internet addiction	2.69 (1.18)	2.73 (1.18)	2.64 (1.18)	0.183

Average scores and standard deviations for the measures of the big five personality traits, the level of moral disengagement about cyberbullying and the ratings of the degree to which participants feel nervous (Internet addiction) when they do not have access to the internet (from 1 to 5). These descriptive statistics were computed both over the entire sample, collapsing across the gender of respondents, and separately for female and male participants. The p-values reported in the rightmost column are derived from independent samples t-test comparing the average scores for male and female participants for each measure.

RQ3. Are different types of cyberbullying behaviours specifically related to different profiles of internet and social media usage?

Use of social media/networks

In Table 3 are reported the frequencies of the responses to the questions about the time participants typically spend on social media every day, the number of social accounts held, and the number of friends/followers. Approximately half of participants (52.8%) declared to spend every day between 1 and 3 hours using social media. Most participants (69%) reported having only one social account per social media, and only 30% of them had more than one social account. More than half of the respondents (68%) reported having from 200 to 500 followers. For each of these variables, we used Chi-square tests to compare the distributions of responses across gender and age.

Table 3

Use of social media.

Variable	N	%
Time spent on social media
Never	5	0.41%
Less than one hour per day	155	12.65%
From 1 to 3 hours per day	647	52.82%
More than 3 hours per day	418	34.12%
Number of social accounts
No account	15	1.23%
One account	844	69.12%
More than one account	362	29.65%
Number of followers
< 200	221	18.42%
200–500	821	68.42%
501–1000	88	7.33%
1001–5000	64	5.33%
> 5000	6	0.50%

Descriptive statistics (frequencies) about the use of social media in the sample (N = 1228).

Girls reported using social media for more than 3 hours per day (41.5%) significantly (χ(3) = 60.38, p < .001) more often than boys (22.5%). No differences were found between age groups.

Girls also reported having more than one account (33.4%) more frequently (χ(2) = 13.40, p = .001) than boys (23.8%). The distribution of the responses about the number of social network accounts varied significantly across age groups (χ(6) = 45.28, p < .001). Having more than one account tended to be more frequent in the lower age groups (14–15 years: 35.3%; 16–17 years: 30%), than in the other groups (18–20 years: 16.6%, 21–25 years: 15.8%), and in the highest age group having no account at all was significantly more frequent (10.5%) than in the other groups (0.8%-1.4%).

The distribution of the number of followers of each participant varied significantly across gender (χ(4) = 46.52, p < .001): girls had more frequently between 200 and 500 friends/followers (74.6%) than boys (58.9%), while boys had more frequently less than 200 followers (27.7%) than girls (12.6%). No differences were found between age groups.

Whatsapp (91.9%) and instagram (91%) were the most used social networks, equally by boys and girls. YouTube was used by 67% of participants, Facebook by 28.8%, Google+ by 22.9%, Snapchat by 14.2% and Pinterest by 10.9%. Boys reported using YouTube (χ(1) = 30.38, p < .001) more than girls. Girls reported using Pinterest (15,20%, χ(1) = 36.18, p < .001), Snapchat (18.53%, χ(1) = 29.88, p < .001) and Tumblr (8.27%, χ(1) = 14.20, p < .001) more than boys (Pinterest: 4.20%, Snapchat: 7.35%, Tumblr: 2.94%).

RQ3. Are different types of cyberbullying behaviours specifically related to different profiles of internet and social media usage?

Correlations between cyberbullying and personality

To investigate the relationships between cyberbullying perpetration, personality traits and other individual variables we first computed a correlational analysis. Table 4 shows Pearson product-moment correlations between personality factors, aggressive behaviours, and moral disengagement about cyberbullying.

Table 4

Product-moment correlations between personality factors, aggressive behaviours, and moral disengagement.

	E	A	C	ES	O	MD
1. sending nasty or indecent messages	0.07	-0.15 ***	-0.19 ***	-0.02	0.04	0.34 ***
2. sending nasty or indecent emails	0.06	-0.05	-0.09	+0.01	0.03	0.29 ***
3. sending embarrassing photos of someone via cell phone	0.06	-0.09	-0.15 ***	-0.02	0.10 *	0.16 ***
4. pretending to be someone else on the Internet	0.01	-0.09 *	-0.20 ***	-0.02	0.06	0.19 ***
		(p = .045)
5. telling someone else’s secrets online or through a cell phone without permission	0.06	-0.06	-0.15 ***	-0.03	0.04	0.13 ***
6. spreading gossip about someone on the Internet	0.11 **	-0.12 **	-0.11 **	-0.01	0.04	0.19 ***
	(p = .009)	(p = .002)	(p = .006)

E = Extraversion. A = Agreeableness. C = Conscientiousness. ES = Emotional Stability. O = Openness. MD = Moral Disengagement.

*p < .05.

** p < .01.

*** p < .001.

Among the personality traits, conscientiousness was significantly and negatively correlated with all the types of cyberbullying but sending mean email. Agreeableness was negatively correlated with sending mean messages, pretending to be someone else and spreading gossip, while openness and extraversion were positively correlated with, respectively, sending embarrassing photos and pretending to be someone else.

Moral disengagement was positively and significantly correlated with all the aggressive behaviours, as expected (r ranging from .16 to .34).

Predictors of cyberbullying behaviours

We investigated the predictors of the self-reported frequency of perpetration of the 6 different cyberbullying behaviours using 6 multiple regression models. We considered 12 variables (demographics, personality, use of social networks, moral disengagement, and internet addiction) as possible predictors of cyberbullying, using treatment coding for all the non-numerical variables, and entering simultaneously all the predictors. Since three categorical variables have 3 levels, each of them was coded using two binary predictors, and the total number of predictors in the models was therefore 15. The analyses were conducted after filtering out data from participants that reported to never use social media, or to not have any accounts on them, or that did not provide answers to the items about cyberbullying perpetration. The few participants in the 21–25 age group were also excluded. Table 5 shows the results of the analysis, in the form of Odds Ratio (OR) and associated p-value for all the predictors, and the estimates of the percentage of variance explained by each model (Nagelkerke’s R2). Being multinomial (ordered) models, an OR significantly greater than 1 for a predictor indicates that the probability that the respondent declared to have never perpetrated a specific form of cyberbullying was associated with, and tended to decrease across, the levels of the predictor (whether it is continuous or discrete). While the probability of having perpetrated the given behaviour with any given frequency, as opposed to never or a lower frequency, tended to increase. The opposite response pattern concerning the frequency of cyberbullying perpetration corresponds to an OR significantly lower than 1. The response probabilities of the different frequencies of perpetrating various forms of cyberbullying, as a function of the significant predictors in the regression analyses, are reported in a series of plots in S1 File.

Table 5

Results of the ordinal regressions models for the responses about the frequency of different cyberbullying behaviours.

	1: Sending mean / indecent messages		2: Sending mean / indecent emails		3: Sending embarrassing photos		4: Pretending to be someone else		5: Revealing secrets		6: Spreading rumors/gossip
Predictors	OR	p	OR	p	OR	p	OR	p	OR	p	OR	p
Gender: M	2.35	<0.001	1.47	0.235	1.32	0.045	0.80	0.199	0.88	0.390	0.93	0.652
Age: 16–17 years (vs 14–15 years)	1.20	0.216	0.77	0.454	0.93	0.614	0.91	0.577	0.97	0.815	1.20	0.273
Age: 18–20 years (vs 14–15 years)	1.36	0.074	1.36	0.422	1.14	0.417	0.91	0.633	0.93	0.650	1.00	0.992
Agreeableness	0.78	<0.001	0.85	0.264	0.90	0.098	0.92	0.287	0.95	0.428	0.82	0.009
Extraversion	1.05	0.509	1.19	0.271	1.04	0.556	0.90	0.191	1.12	0.089	1.23	0.008
Conscientiousness	0.79	<0.001	0.86	0.283	0.81	0.001	0.73	<0.001	0.83	0.005	0.88	0.099
Emotional Stability	0.95	0.490	1.01	0.931	1.04	0.555	1.14	0.103	1.09	0.204	1.05	0.568
Openness	1.10	0.141	1.08	0.598	1.24	<0.001	1.14	0.074	1.13	0.049	1.12	0.137
N. Followers: < 200 (vs 200–500)	0.83	0.291	0.98	0.951	0.61	0.004	0.67	0.059	0.59	0.004	0.75	0.184
N. Followers: > 500 (vs 200–500)	1.01	0.967	0.71	0.431	0.86	0.409	0.96	0.847	0.74	0.106	1.28	0.228
Time on SN: < 1 h/day (vs 1–3 h/d)	1.09	0.680	0.35	0.171	0.68	0.063	0.62	0.103	0.56	0.012	0.82	0.452
Time on SN: > 3 h/day (vs 1–3 h/d)	1.44	0.013	1.11	0.754	1.00	1.000	1.00	0.990	1.08	0.573	1.17	0.332
N. of SN accounts: > 1 (vs 1)	2.15	<0.001	1.62	0.113	1.60	0.001	2.98	<0.001	1.46	0.007	2.03	<0.001
Internet addiction	1.07	0.238	1.17	0.246	1.17	0.005	1.16	0.024	1.24	<0.001	1.06	0.358
Moral Disengagement	1.78	<0.001	2.07	<0.001	1.28	<0.001	1.45	<0.001	1.24	0.001	1.48	<0.001
Observations	1120		1120		1120		1120		1120		1120
R2 Nagelkerke	0.226		0.163		0.111		0.1777		0.106		0.126

The values in this table correspond to the Odds Ratios (OR, and associated p-values) for the effects of different predictors on the self-reported frequency of 6 different cyberbullying behaviours, estimated by fitting (for each behaviour) an ordinal multiple regression. All the predictors were entered simultaneously in the regression models, in which the dependent variables were ratings of the reported frequency of the perpetration of the behaviours by the respondents, expressed on a 5-point frequency scale (from “never” to “more than 5 times”). SN = Social Networks.

As it can be seen in the table, among the predictors considered in the analysis only moral disengagement was significantly associated with all the forms of cyberbullying (with OR ranging from 1.24 to 1.78, p < .001). Different predictors were in fact associated with different behaviours.

Age was not associated with any behaviour. Gender was associated with sending mean or indecent messages, whose odds were 2.35 times higher for boys than for girls (p < .001), and with sending embarrassing photos (OR = 1.32, p = .045). Other significant predictors for this behaviour, both with ORs smaller than 1 indicating a protective effect, were conscientiousness (OR = 0.79, p < .001) and agreeableness (OR = 0.78, p < .001). Besides these personality traits the results of the analysis showed that the likelihood of sending mean messages was significantly higher for those spending more than 3 h/d on social networks (OR = 1.44, p = .013) than for those spending 1 to 3 h/d on them, and for those who reported to have more than one profile on social networks (OR = 2.15, p <. 001). Overall, the model explained about 23% of the variance in the frequency of this type of cyberbullying.

The likelihood of sending mean or indecent emails was only significantly predicted by moral disengagement (OR = 2.02, p < .001). Sending embarrassing photos was less likely perpetrated by girls, individuals with higher conscientiousness (OR = 0.81, p = .001) and fewer followers (OR = 0.61, p = .004), and was more likely perpetrated by those with higher openness (OR = 1.24, p < .001), having multiple accounts on some social networks (OR = 1.60, p = .001) and higher addition to the internet (OR = 1.17 p = .005).

Pretending to be someone else online when interacting on social networking sites was less likely in more conscientious individuals (OR = 0.73, p < .001) and more likely in those having multiple accounts on some social networks (OR = 2.98, p < .001) and higher addition to the internet (OR = 1.16 p = .024).

Revealing secrets about someone online was also less likely in more conscientious individuals (OR = 0.83, p = .005) and in those having less than 200 followers (OR = 0.59, p = .004) and spending less than 1h/d on social networks (OR = 0.56, p = .012), and more likely in more open individuals (OR = 1.13, p = .049), in those who reported more getting nervous without internet (OR = 1.24, p < .001), and in those having more than one profile on social networks (OR = 1.46, p = .007).

Lastly, the likelihood of spreading rumors or gossip online was higher in more extraverted individuals (OR = 1.23, p = .008) and in those having more than one profile on some social network sites (OR = 2.03, p < .001), while it was less likely in more agreeable respondents (OR = 0.82, p = .009).

RQ4. Is cyberbullying better conceived and measured as a unitary construct or as a multidimensional construct?

Latent variable analyses

In the light of the results of the regression analysis, we examined the factorial structure of the cyberbullying scale that we used [51], comprising the items with the frequency of different cyberbullying behaviours. We randomly split the dataset into a training subset (30%, N = 336) and a test subset (70%, N = 784). On the training subset we conducted an Exploratory Factor Analysis, using minimal residual for factor extraction and oblimin rotation. Polychoric correlations were used for the analysis, and parallel analysis was used to determine the number of factors to retain. The model extracted comprised two factors, each loading on 3 items (Table 6): one -Cyb1- loaded on sending mean messages (loading = 0.66), sending mean emails (loading = 0.77), and impersonating someone else online (loading = 0.43), and the other one -Cyb2- loaded the remaining three cyberbullying behaviours (sending embarrassing photos: loading = 0.42, revealing secrets: loading = 0.93, spreading rumors/gossip: loading = 0.60). The factors were positively correlated (r = 0.43), and together explained 52% of the variance.

Table 6

Results of the exploratory factor analysis of cyberbullying perpetration.

Cyberbullying behaviour (item)	Cyb1	Cyb2	u
1. Sending nasty or indecent messages	0.66		0.55
2. Sending nasty or indecent emails	0.77		0.44
3. Sending embarrassing photos of someone via cell phone		0.42	0.58
4. Pretending to be someone else on the Internet	0.43		0.65
5. Telling someone else’s secrets online / by cell phone without permission		0.93	0.19
6. Spreading gossip about someone on the Internet		0.60	0.49
Proportion of variance explained (R 2 )	0.25	0.27

Results of the exploratory factor analysis of the cyberbullying perpetration items behaviour items, conducted on a randomly selected subset of participants (N = 336). For each item we report the loadings on each of the two latent factors extracted in the analysis and the proportion of unique item variance (u). For each factor we also report the relative proportion of explained variance. The loadings presented correspond to the rotated solution (pattern matrix). Factor loadings < .4 were omitted from the table.

On the test set we then conducted two Confirmatory Factor Analyses (CFA), one using the single factor model used in the Meter and Bauman scale [51], and the other using the 2-factor model suggested by the EFA. Both models had a very good fit to the data, but the 2-factor model had slightly better fit according to all the fit measures used [1 factor: Chi(9) = 19.027, p = .025, N = 784, CFI = 0.991, NFI = 0.984, NNFI = 0.986; 2-factor: Chi(8) = 11.175, p = 0.192, CFI = 0.997, NFI = 0.991, NNFI = 0.995].

The latent cyberbullying factors were used in a Structural Equation Modelling analysis (SEM) to test hypotheses about the direct and indirect effects of personality, moral disengagement, and internet addiction on the different types of cyberbullying. The structural model tested in the analysis is presented in Fig 3 along with the standardised estimates of all the path coefficients (β) (model fit statistics are reported in the caption).

Fig 3

Structural equation model of the relationships between personality, moral disengagement, and cyberbullying factors.

Ovals and rectangles represent latent and manifest variables respectively. Standardized path coefficients are reported for all the direct effects tested in the model, and the colours of the paths represent the sign of the coefficients (orange = positive; blue = negative). Insignificant effects are represented by dashed arrows. [N = 1139, χ2(141) = 358.64 (p = .000); RMSEA = .037 (90% CI = [.032, .042]); NNFI = .977; CFI = .964].

As it can be seen, and consistently with the previous analyses, each of the cyberbullying factors was associated with some, but not all, of the BFI personality traits. And whenever a trait was significantly associated with both cyberbullying factors, the strength of association (as measured by the standardised path coefficients) varied across factors. In most cases, the strength of the association was greater for the first factor than for the second one. This was particularly the case for the positive effect of moral disengagement, but also for the negative (i.e. protective) effect of conscientiousness and agreeableness. Conversely, the personality factors that were positively associated with cyberbullying tended to be associated only (i.e. extraversion) or more strongly (i.e. openness) with the second cyberbullying factor. Overall, the model was able to explain 50% of the variance in the first latent cyberbullying factor, and 24% in the second one.

Given that all the personality traits but agreeableness were also significantly associated with moral disengagement, explaining about 4% of the variability in this latent variable, we tested the indirect effects of each personality factor on the cyberbullying factors mediated by their effect on moral disengagement. In Table 7 are reported the results of these tests. As it can be seen in the table, all the personality factors that were directly associated with moral disengagement had a significant indirect effect on the cyberbullying factors. The tests of the total effects of the personality factors on cyberbullying were significant except for the one of emotional stability, and confirmed that agreeableness and conscientiousness tended to reduce cyberbullying, particularly of the type involving direct aggression toward others online (cyberbullying factor 1), while openness and extraversion tended to increase cyberbullying, particularly of the type involving damages to someone’s reputation or public image (cyberbullying factor 2).

Table 7

Results of the mediation analysis from the structural equation modelling analysis.

Direct effects			Indirect effects			Total effects
Effect	Est.	p	Effect	Est.	p.	Effect	Est	p.
A → CBY 1	-0.157	< .000	A → MD → CYB 1	-0.030	0.139	A → CBY 1	-0.186	< .000
C → CYB 1	-0.184	< .000	C → MD → CYB 1	-0.076	< .000	C → CYB 1	-0.260	< .000
ES → CBY 1	-0.010	0.795	ES → MD → CYB 1	0.065	0.002	ES → CBY 1	0.054	0.191
O → CYB 1	0.122	0.002	O → MD → CYB 1	-0.040	0.033	O → CYB 1	0.081	0.048
E → CYB 1	0.029	0.454	E → MD → CYB 1	0.053	0.009	E → CYB 1	0.082	0.041
A → CBY 2	-0.100	0.011	A → MD → CYB 2	-0.017	0.146	A → CBY 2	-0.117	0.003
C → CYB 2	-0.140	< .000	C → MD → CYB 2	-0.042	0.001	C → CYB 2	-0.182	< .000
ES → CBY 2	–0.018	0.650	ES → MD → CYB 2	0.036	0.004	ES → CBY 2	0.018	0.633
O → CYB 2	0.147	< .000	O → MD → CYB 2	-0.023	0.040	O → CYB 2	0.124	0.001
E → CYB 2	0.112	0.005	E → MD → CYB 2	0.030	0.015	E → CYB 2	0.141	< .000
A → MD	-0.048	0.135
C → MD	-0.134	< .000
ES → MD	0.112	0.001
O → MD	-0.070	0.033
E → MD	0.092	0.008
MD → CYB 1	0.577	< .000
MD → CYB 2	0.323	< .000

To test differences between groups defined by gender (females vs males), time on social networks (+3 h/d vs 1–3 h/d) and number of profiles on social networks (1 profile vs >1 profile) in the mean score of the latent factors (the two cyberbullying factors and moral disengagement), and to test possible moderating effects of each of the grouping factors on the direct and indirect effects of personality and moral disengagement on the cyberbullying factors, three multigroup SEM analyses were conducted, one for each of the grouping variables. In these analyses, first we always tested measurement invariance (configural, weak and strong), since comparisons across the groups in the mean scores of the latent variables and in the regression paths could only be meaningful if strong invariance holds.

The results of invariance tests, and the parameters of the multigroup models for each group and grouping factor are provided in S2 File. Strong measurement invariance held across groups for each grouping factor, indicating that it was possible to compare latent variables means and effects estimates across groups.

The multigroup analysis about gender revealed significantly higher mean levels of moral disengagement (diff. = 1.782, p = .010) for males than for females. Moreover, the results showed that gender significantly moderated both the effect of openness on the first cyberbullying factor (slope females = 0.198, slope males = 0.029, diff. = -0.169, p = .026), and the effect of extraversion on the second cyberbullying factor (slope females = 0.047, slope males = 0.219, diff. = 0.171, p = .034).

The mean of moral disengagement was significantly higher in those spending 1–3 hour per day on SN than in those spending more than 3 h/d (diff. = 1.168, p = .039), and time on SN was found to be a moderator of the direct effect of emotional stability on moral disengagement (slope +3 h/d = 0.217, slope 1–3 h/d = 0.005, diff. = 0.212, p = .005) and of the indirect effects of this personality trait on the cyberbullying factors (cyberbullying factor. 1: diff. = 0.133, p = .004; cyberbullying factor 2: diff. = 0.071. p = .025).

Lastly, the results of the multigroup analysis involving the number of social network profiles revealed significantly higher mean levels of moral disengagement in those with more than one profiles than in those with only one social network profile (diff. = 1.270, p = 0.045), but no differences in the mean of the latent cyberbullying factors. Moreover, the number of profiles significantly moderates the direct effect of extraversion on moral disengagement (slope 1 profile = 0.142, slope >1 profiles = -0.022, diff. = 1.64, p = .020) and the indirect effect of this personality factor on the cyberbullying factors mediated by moral disengagement (cyberbullying factor. 1: diff. = 0.98, p = .040; cyberbullying factor 2: diff. = 0.059. p = .043).

Discussion

The results of our study showed that different types of aggressive online behaviours are, reportedly, enacted with differential frequency by adolescents, confirming H1. For all the forms of cyberbullying considered, most participants reported they have never committed them, and for some behaviours, such as sending mean or aggressive emails, even reports of having perpetrated them sometimes were extremely rare. This last result is possibly related to uncommon use of emails among adolescents [74]. But other types of cyberbullying, like aggressions by text messages or sending embarrassing photos of someone, were perpetrated at least sometimes by more than 40% of participants, and often (≥ 3 times) by 13–18%. Gender differences in the distribution of cyberbullying frequency were found only for some specific forms of cyberbullying, again sending mean messages or embarrassing photos, therefore H2 is also confirmed by the results. However, differently from other studies (see, for example, [54, 75, 76]) no differences across age groups were found. This result is probably due to the narrow age range of our sample that did not include children, in which young adults were almost entirely absent.

Moral disengagement on cyberbullying was strongly associated with all forms of cyberbullying, confirming previous findings [13, 51, 52], while personality was not: conscientiousness was the personality trait that was associated with the highest number of cyberbullying behaviours (4 out of 6, all but sending mean emails and spreading rumor/gossip), in each case having a protective effect (i.e. more conscientious individuals reported less frequent perpetration) [77]. Agreeableness also had a protective effect [42], but only on sending mean messages. Openness was instead positively associated with sending mean messages, sending embarrassing photos and revealing secrets, and extraversion with spreading rumors/gossip. Emotional stability was not associated with any behaviours, in contrast to what has been previously reported by one study [65]. However, in the SEM analysis we found significant positive indirect effects of emotional stability on both cyberbullying factors, mediated by moral disengagement. The findings about a positive association between emotional stability and moral disengagement also contrast with previous findings [78]. The SEM analysis also confirmed that the effect of moral disengagement was stronger on cyberbullying acts involving direct aggressions than on acts damaging social image or reputation. Regarding the relationships between personality traits and different cyberbullying behaviours, it is interesting to note that no two behaviours are associated with the same traits. This suggests that the personality structures underlying the different behaviours may be quite specific (H3).

The results regarding the association between time spent online and engaging in virtual offensive behaviours is particularly relevant as it becomes clearer and clearer how internet use is increasing across cultures [79]. The ordinal regression analysis showed, first of all, that the daily amount of time spent on social networks is predictive only of some cyberbullying behaviours, not all, confirming H4. This specific relationship between the frequency of internet use and specific cyberbullying behaviours had not been highlighted by other studies that, instead, had indicated a generic relationship [42, 68]. Specifically, in our findings the likelihood of sending mean messages (with some frequency) tended to be higher in those that spend more than 3 h/d on social networks than in those that spend between 1 and 3 h/d, while the likelihood of sending embarrassing photo and revealing secrets tented to be lower in those that use social networks less than 1 h/d. Moreover, the results of the multigroup SEM analysis showed that time on social networks moderated the indirect effect of emotional stability on both the cyberbullying factors, which was only significant and positive (opposite in sign to what previous research would have let to expect) for those spending more than 3 h/d on social networks.

Lower likelihood of perpetrating the latter two behaviours was also found in individuals with few followers/friends on social networks. It is possible that these factors were associated with cyberbullying because spending more or less time online, or having a wider or a smaller social network simply increases or decreases the occasions for acts of cyberbullying. However, it is not clear why this association was only found with these specific behaviours and not for all. Among the social media use variables that we considered, at least one was however positively and strongly associated with almost all the cyberbullying behaviours: having more than 1 profile on social networks, a factor that increased the odds of cyberbullying between 46% and 198%, depending on the behaviour considered. Only the likelihood of sending mean/indecent emails was not associated with this factor. It might be that having more profiles on social networks is an enabling factor for cyberbullying, a means for perpetrating aggressive acts online in disguise trying to escape possible consequences of the acts. It is worth noting that the strongest association was found between this factor and pretending to be someone else online. But it is also possible that some other individual factor, not measured in the current study, might be responsible for the association. It is also interesting to notice that the multigroup SEM analysis showed higher levels of moral disengagement in those having more than 1 profile on social networks, and also that this factor moderated (increased) the indirect effects of extraversion on cyberbullying mediated by moral disengagement. Further studies should try to investigate this matter more in depth.

Lastly, our analysis highlighted an association between internet addiction and some bullying behaviours [77], such as sending embarrassing photos, revealing secrets, and pretending to be someone else online, whose frequency was higher in individuals that tended to be nervous without access to the internet. These are three behaviours that, in addition to moral disengagement, are all associated with a low level of conscientiousness. This makes it possible to consider a relationship between the diminished capacity for self-control, self-regulation and impulse control and forms of internet addiction. This consideration is in line with what has been highlighted by a meta-analysis in which it emerges that among the factors that influence internet addiction, in addition to the measuring instrument used, and the cultural area of reference, there is the young age of the participants [80]. Unfortunately, however, in our study, the degree of internet addiction was measured with a single item. Therefore, it is incautious to go into more fine-grained explanations of the relationship between internet addiction and the enactment of specific aggressive virtual behaviours.

Our last research question concerned the measurement of cyberbullying, and specifically whether it should be conceived and measured as a unitary or a multidimensional (H5) construct. The results of our latent variable analyses all seem to point toward a multivariate nature of cyberbullying, confirming H5. The exploratory factor analysis suggests that one could distinguish between a component related to harmful acts that an individual might commit to someone else directly (e.g. offending the target by direct messages, emails, or deceiving someone about their identity), and a component related to acts that might hurt someone even without a direct interaction and by damaging the target’s social image (i.e. acts in which harm require the involvement of a social audience). And the confirmatory factor analysis showed that the 2-factor structure has better fit than the single factor solution. Interestingly, the results of the SEM analysis suggested that these factors might be differentially affected by personality and moral disengagement. Direct aggressive acts (Cyberbullying factor 1) are more strongly associated with moral disengagement, with having multiple SN profiles, and with personality traits such as conscientiousness and agreeableness (both playing a mitigating role) than acts that harm someone indirectly (Cyberbullying factor 2). The latter form of cyberbullying, conversely, is particularly associated with extraversion and openness. Moreover, only for direct aggressive acts we found differences between female and male cyber-aggressors. Overall, these findings suggest that indeed cyberbullying might not be a unitary phenomenon, as other studies also had indicated [34, 43, 62, 63, 81–83] and that differences in the findings from the research literature about the association between cyberbullying and various demographic and individual variables could be the results of differences in the specific measurement of cyberbullying used across studies. Differentiating distinct forms of cyberbullying with different antecedents or moderators could help to identify in early evaluations which subjective and contextual characteristics can more easily lead to the emergence of specific offensive virtual acts. And from this, can descend not only the understanding of the more detailed forms of cyberbullying, but also the design of interventions aimed more effectively and more promptly at its prevention and mitigation.

Limitations

The study reported in this paper has some limitations that need to be acknowledged. The first limitation concerns the type of measurement of cyberbullying used in the study, which was self-reported, and therefore possibly affected by biases such as the social desirability effect, the tendency to provide responses that give a better impression of oneself as a person and member of society [84, 85]. When it comes to reporting the frequency of perpetrating aggressive behaviours, it is likely that this phenomenon might have been at play, and, in this study, it was not controlled for. Even if that was the case, however, this bias should not undermine our findings about predictors of cyberbullying, but only cause our estimates of the frequency of the phenomenon among adolescents to be lower than the actual frequency. Nonetheless, repeating this study using observational data as an outcome, possibly collected from adolescents’ activity on social networks (posts, tweets, comments, photos or other forms of shares directly or indirectly involving a victim), would increase the validity of the results and the confidence in our findings.

The second limitation of this study lies in the cross-sectional nature of the survey, which only allows us to draw conclusions about statistical associations between individual variables and cyberbullying, preventing us from being able to infer causal relationships. While for ethical reasons studying this phenomenon experimentally would be very difficult, if not unfeasible, at least a longitudinal design could partly help to overcome this limitation concerning internal validity.

A third limitation is related to the way some variables were measured. On the one hand, in fact, one of the predictors that were statistically associated with more bullying behaviours was internet addiction, which was only measured with a single item on a 5-point Likert scale. The effect of this variable on cyberbullying should be thus investigated more in depth, adopting more articulated measures, to validate and possibly extend our findings. On the other hand, even the scale we adopted to measure cyberbullying was quite specific and considered a limited number of behaviours only. This is an important limitation of the study. It is still to be determined, and certainly worth investigating, whether our findings about the multidimensional nature of cyberbullying would be found also using different measures and scales.

Lastly, our study was conducted on a sample of Italian adolescents, and although there is no reason to believe that our findings shouldn’t generalize to different cultural contexts, further studies should still be conducted on an international sample to empirically test this hypothesis.

Conclusions

Considering recent knowledge advances on cyberbullying, including the findings from the study presented in this paper, it seems reductive to qualify this offensive behaviour as a unitary phenomenon. Rather, considering the different variables involved, it seems more likely that there are different expressions of cyberbullying, offensive behaviours that can be differentiated by considering socio-demographic variables, personality characteristics, and the different relational tools available on the Internet.

The study presented here considered a scale with six items. For each of these items, which correspond to specific offending behaviours, different correlates of age, personality, and social media interaction were highlighted. From our results, it was also possible to put forward a conceptual framework that describes cyberbullying as a harmful behaviour that can have at least two facets: one related to offenses perpetrated directly to the victim, the other less explicit, which can involve other people and is indirect. It is legitimate to consider that with different scales, and with a greater number of items, it would be possible to point out further aspects of cyberbullying. In any case, the results obtained and the theoretical model proposed call for deeper investigations of cyberbullying, not only for its different manifestations, but also for individual and contextual reasons that make it occurring.

Questionnaire.

Full text of questionnaire used in the study.

(PDF)

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R script for conducting the descriptive statistics reported in the paper.

(R)

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R for conducting the regression analyses reported in the paper.

(R)

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R script for conducting the exploratory and confirmatory factor analyses reported in the paper and the latent variable path analysis / SEM.

(R)

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R Dataset for descriptive statistics.

Data are in native R format.

(RDATA)

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R Dataset used for the regression and SEM analyses.

Data are in native R format.

(RDATA)

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Dataset used for the regression and SEM analyses.

Data are in native comma separated value format.

(CSV)

Click here for additional data file.

Plots of the effects of the significant predictors in the ordinal logistic regression models.

Each plot represents the estimated probability of the different responses concerning the frequency of perpetration of a given type of cyberbullying behaviour, as a function of one of the significant predictors. The probabilities were estimated from the fitted regression models presented in the paper. All the code for reproducing the plots is provided in S2 File.

(PDF)

Click here for additional data file.

Multigroup SEM analyses.

The results of the multigroup analyses for three grouping factors are reported in this file: a) gender (females vs males), b) time on social network (>3 h/d vs 1–3 h/d), and number of social networks profiles (1 profile vs >1 profiles). For each factor we present the goodness-of-fit statistics of models assuming different levels of invariance and the path coefficients for the model assuming strong invariance.

(PDF)

Click here for additional data file.

4 Feb 2022

PONE-D-21-37456

How many cyberbullying(s)? A non-unitary perspective for offensive online behaviours

PLOS ONE

Dear Dr. Guidi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we have decided that your manuscript does not meet our criteria for publication and must therefore be rejected.

Specifically, the reviewers expressed concerns with the methodology, data analysis and reports of the findings.

I am sorry that we cannot be more positive on this occasion, but hope that you appreciate the reasons for this decision.

Yours sincerely,

Mingming Zhou, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear author and editor,

I consider this research to be rigorous and useful to the scientific community.

However, we should improve some aspects to favor the reader's understanding. These are minor revisions.

Abstract: add all sociodemographic data, sex and age.

Theoretical framework: add more meta-analysis references that support your theoretical framework. Here are some studies that could be interesting:

Pan, Y. C., Chiu, Y. C., & Lin, Y. H. (2020). Systematic review and meta-analysis of epidemiology of internet addiction. Neuroscience & Biobehavioral Reviews, 118, 612-622. https://doi.org/10.1016/j.neubiorev.2020.08.013.

Modecki, K. L., Minchin, J., Harbaugh, A. G., Guerra, N. G., & Runions, K. C. (2014). Bullying Prevalence Across Contexts: A Meta-analysis Measuring Cyber and Traditional Bullying. Journal of Adolescent Health, 55(5), 602-611. https://doi.org/10.1016/j.jadohealth.2014.06.007

Marciano, L., Schlz, P. J., & Camerini, A. L. (2020). Cyberbullying perpetration and victimization in youth: A meta-analysis of longitudinal studies. Journal of Computer-Mediated Communication, 25(2), 163-181. https://bit.ly/3iK8b0g

Lei, H., Li, S., Chiu, M. M., & Lu, M. (2018). Social support and Internet addiction among mainland Chinese teenagers and young adults: A meta-analysis. Computers in Human Behavior, 85, 200-209. https://doi.org/10.1016/j.chb.2018.03.041. https://doi.org/10.1016/j.chb.2018.03.041

Lozano-Blasco, R., Cort'es-Pascual, A., & Latorre-Martínez, M. P. (2020). Being a cybervictim and a cyberbully - The duality of cyberbullying: A meta-analysis. Computers in Human Behavior, 111, 106444. https://doi.org/10.1016/j. chb.2020.106444.

1. Lozano-Blasco, R.; Cortés-Pascual, A. Problematic Internet uses and depression in adolescents: A meta-Analysis. Comunicar 2020, 28, 109-120. [Google Scholar] [CrossRef].

Holt, M. K., Vivolo-Kantor, A. M., Polanin, J. R., Holland, K. M., DeGue, S., Matjasko, J. L., Wolfe, M., & Reid, G. (2015). Bullying and Suicidal Ideation and Behaviors: A Meta-Analysis. PEDIATRICS, 135(2), e496-e509. https://doi.org/10.1542/peds.2014-1864.

Methodology: add a main figure where the procedure and research design are visually explained.

Participants: write the sociodemographic information in a table to make it more visual. Clarify the regions that have been involved in Italy, a heat map would bring innovation and creativity.

Instruments: very well developed but do not forget to put the Italian version next to the name. In the references it is fine, but it is important for the scientific community to see that you have used standardized tests adapted to their culture.

Statistical analysis: very well developed, do you think that mediation analysis could provide more information on the moderating variables?

Results: very interesting but Figure 1 has poor visual quality. The concepts are very good but it looks very blurry and the size of the numbers is very small. The same happens with figure 2. Try to use some application like Canva, Flourish that allows you to improve the quality.

Discussion: I liked it very much, but to give it a plus of quality I recommend that you review more references on the use of social networks and cyberbullying. Add more meta-analysis studies that allow you to give more robustness to your claims. Adding the hypotheses in the text was a great idea. The limitations are very sincere and clear, but I miss two paragraphs on practical implications this research would have.

Conclusions: they are great, but you usually don't put references in this section, could you upload those references to discussion and rewrite those sentences in a more general way?

Supplementary material: it is very honest that you upload the questionnaire, but it is an opportunity for this to be used by others. I would encourage you to upload the data of your rating along with it, so that it can be used.

Once again, thank you for your thoroughness and good work.

Reviewer #2: Despite the great efforts, authors are advised to make substantial changes to the manuscript as stated below. Because the review found some limitations regarding methods and results, comments on the discussion section are not addressed.

2. Methods

- Please mention the data collection period and explain the sampling method used

- Were the participants in the study all high school students? It seems like adolescents and young adults are mixed as authors included the age range from 14 to 25.

- Where is the reference to the use of social networks and feelings related to network use questionnaires? Please provide the original reference and how they were modified.

- Are the scales used in the survey all well-validated and -translated in Italian? If not validated, how was it applied to the survey? Did the researcher manually translate the items? If the scales are modified from the original version, please indicate both the original and revised version.

3. Results

- It seems like the results of chi-square is only reported in the text, did the authors not create table?

- Authors attempted to test several hypotheses but having so many hypotheses often mislead the readers to lose focus of the study. To make the results section more reader-friendly, it is recommended for the authors to match the results with the corresponding hypotheses so that they/we could understand why these results are presented in this section.

- When reviewing the methods section, I thought six items used to measure cyberbullying would be combined as a single variable instead of splitting each item. If the purpose of the current study was to measure the different types of cyberbullying, it was more appropriate to choose a scale consisting of several subscales rather than splitting a single measurement tool by items. This is a big limitation therefore it should be clearly stated in the limitation section.

- I don’t quite understand the intention behind conducting the EFA, CFA, and structural equation modelling in this study. This study did not develop a scale nor did it validate the existing scale. Why should these analyses be performed? For what purpose?

- Figure 2 is very confusing, and it is not a typical arrangement for SEM. Please refer back to other SEM research to redraw the figure. Why is there no latent variable for openness, extraversion etc. and for social network uses? Just by looking at this figure, the DV and IV are unclear.

Lastly, the sentences are way too long in some paragraphs. Please polish the language in order to improve the overall quality of the manuscript.

Reviewer #3: This paper conducted a questionnaire that was administered to a large sample of high school students to validate the four hypotheses regarding cyberbullying. These research questions are interesting and provide insights about cyberbullying behavior. For example, statistical analyses in this work show that cyberbullying is not a unitary construct but a multidimensional construct.

I have following suggestions to further improve the manuscript:

1. Given the extreme popularity of AI and Machine Learning, in related work, the authors might consider including recent works that used machine learning models to validate similar findings. For example, in [1], the authors showed that user's personality traits and peer influence are important predictors of cyberbullying. In [2], the authors also showed capturing the repetitive pattern in cyberbullying behavior can improve the performance.

2. The figures are very low-quality and it is hard to read. The authors need to replace them with high-resolution figures.

3. The cyberbullying definition is not clear, and sometimes confuses with other similar concept, such as cyber-aggression [3]. The authors need to clarify these differences.

[1] PI-bully: Personalized cyberbullying detection with peer influence

[2]Hierarchical attention networks for cyberbullying detection on the instagram social network

[3]Cyberbullying: Bullying in the digital age

**********

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Raquel Lozano Blasco

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

- - - - -

For journal use only: PONEDEC3

Submitted filename: How many cyberbullying(s).docx

Click here for additional data file.

29 Mar 2022

Response to Academic Editor

C.1. Dear Dr. Guidi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we have decided that your manuscript does not meet our criteria for publication and must therefore be rejected.

Specifically, the reviewers expressed concerns with the methodology, data analysis and reports of the findings.

R.E.1. We are sincerely puzzled by this editorial decision, which goes against the suggestions of the reviewers, none of which suggested a rejection decision on our manuscript. Actually, two reviewers (Reviewer 1 and 3) only asked for minor revisions (one going as far as describing our research "rigorous and useful to the scientific community", calling the statistical analysis section "very well developed", the results "very interesting" and the conclusions "great"!), and provided each a few suggestions for improving the manuscript, that we have addressed in the revised manuscript attached (in track changes).

The third one (Reviewer 2) requested substantial changes to the manuscript and raised a number of points concerning the methods and results sections, to which we also have responded pointly in the dedicated section and in the revision.

Additional comments after authors’ query:

[...] C.2 I share the same concern with Reviewer 2 about the methodology and result section. Without validating the selected scales in the first place, it is really hard to say how convincing the findings would be.

R.E.2. We thank the Editor for clarifying the reasons for her decision in response to our query. We perfectly understand the importance of validation of a measure, and agree with the Editor about it being a requirement. But we do not believe that in our study lack of validity could be an issue. First of all in our case concerns could only apply to the validity of the Italian translation of some scales (the cyberbullying and moral disengagement for cyberbullying), because the original versions had been validated.

But our own data from this study (CFA), as well as from a previously published study (https://link.springer.com/chapter/10.1007/978-3-030-49570-1_20) by our group (in which SEM models were used showing very good fit of the measurement model for both the bullying and for moral disengagement scales used in the manuscript we submitted to PlosONE) in our view already provide evidence of the validity of the Italian translation we had previously devised and adopted in this study.

But this wouldn't be the first time that PlosONE published papers concerning studies in which some scales that were not validated in Italian were used. Just to cite a couple of papers I could list this

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0243194 that used the Italian version of the WHO-5 well-being scale that, while widely used, to our knowledge has never been validated in a dedicated study.

Or this other study https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142715 in which the adapted Italian version of a bullying/victimization scale was used, a scale that although used in other studies was never validated (we traced the references two levels down to verify).

Lastly, we note that Reviewer 2 did not consider that our manuscript should be rejected for lack of validity, but asked for more information, and as we also responded to the reviewer in the dedicated section, we have now revised the manuscript to include more information about this.

C.3. Also, the examination of the factorial structure of the cyberbully scale (RQ4, which looked to me less as an independent research question but more as a necessary step of the data analysis) found both one-factor and two-factor model made good fits to the model. The adoption of the two-factor model thus needs a stronger justification. Is there any conceptual meaning of each of these two factors? I wonder if the same two-factor structure could be replicated with another sample. Theoretical underpinning would be necessary to support this two-factor model.

R.E.3. Concerning the 2-factor model, we do believe both that the factors have conceptual meaning (as clearly stated in the manuscript, one is mainly related to Direct aggressions toward someone else online, and the other to Behaviours that damage the social image or reputation of someone else) and also that the procedure to test this 2 factor solution is valid and could be replicated in another sample providing the same results. After all, the EFA was conducted on a random subset of a dataset that comprised responses from a wide and geographically diverse sample of Italian students (randomly split in a smaller train and a larger test subsamples). And CFA confirmed the extracted factor structure in another sample. This analytic strategy is quite common in psychology research [1, 2]. It would have been different (and invalid) if we had run the EFA and the CFA on the same sample, but we did not, and our procedure is also indicated as a viable solution in [3].

To further test the replicability of the factor structure, we have now repeated 100 times the parallel analysis (to determine the number of factors) and the EFA on 100 random samples of N = 336, extracting in each case 2 factors, and comparing the factors structures extracted (the pattern of loadings) to the one in the model reported in the manuscript. In the majority of cases (55%) parallel analysis suggested 2 factors (1 factor was suggested only in 2% of the cases) and in 81.8% of the 2-factor models one or both factors overlapped with the 2 factors in the model that we reported. In other words, it seems to us that the model that we reported is both conceptually and empirically grounded. Moreover, a 2-factor model like ours could help explain inconsistencies in the findings in literature about the association between cyberbullying and other measures of social media use and personality.

[1] Igarashi, H., Kikuchi, H., Kano, R. et al. The Inventory of Personality Organisation: its psychometric properties among student and clinical populations in Japan. Ann Gen Psychiatry 8, 9 (2009). https://doi.org/10.1186/1744-859X-8-9

[2] Matsudaira T., Fukuhara, T., Kitamura, T., (2008). Factor structure of the Japanese Interpersonal Competence Scale. Psychiatry and Clinical Neurosciences. 62(2), 142-151. https://doi.org/10.1111/j.1440-1819.2008.01747.x

[3] Fokkema, M., & Greiff, S. (2017). How performing PCA and CFA on the same data equals trouble: Overfitting in the assessment of internal structure and some editorial thoughts on it [Editorial]. European Journal of Psychological Assessment, 33(6), 399–402. https://doi.org/10.1027/1015-5759/a000460

C.4. It also appears that this two-factor model was not well reflected in Table 3 and 4.

R.E.4. We understand that in Table 4 and 5 (formerly Table 3 and 4) a clear pattern in cyberbullying association with demographic, personality and social media use variables is difficult to highlight. However, in Table 4 items 1 and 4 (both in Factor 1) show the same pattern of correlations with personality and MD, and in Table 5 item 3 and 5 (both in factor 2) are affected by the same predictors, and item 1 and 6 (in different factors in our model) have different pattern of significant predictors. Moreover, the results of the structural equation modeling analysis indicated that different variables are associated with the two different cyberbullying factors.

Response to Reviewer 1

C1.1. I consider this research to be rigorous and useful to the scientific community.

However, we should improve some aspects to favor the reader's understanding. These are minor revisions.

R1.1. We are thankful for the reviewer’s appreciation of our work, and for all the suggestions for improvement that we have followed in the revision process, as detailed in the following points.

C1.2. Abstract: add all sociodemographic data, sex and age.

R1.2. We have added sex, age and geographical area information in the abstract.

C1.3. Theoretical framework: add more meta-analysis references that support your theoretical framework. Here are some studies that could be interesting:

Pan, Y. C., Chiu, Y. C., & Lin, Y. H. (2020). Systematic review and meta-analysis of epidemiology of internet addiction. Neuroscience & Biobehavioral Reviews, 118, 612-622. https://doi.org/10.1016/j.neubiorev.2020.08.013.

Modecki, K. L., Minchin, J., Harbaugh, A. G., Guerra, N. G., & Runions, K. C. (2014). Bullying Prevalence Across Contexts: A Meta-analysis Measuring Cyber and Traditional Bullying. Journal of Adolescent Health, 55(5), 602-611. https://doi.org/10.1016/j.jadohealth.2014.06.007

Marciano, L., Schlz, P. J., & Camerini, A. L. (2020). Cyberbullying perpetration and victimization in youth: A meta-analysis of longitudinal studies. Journal of Computer-Mediated Communication, 25(2), 163-181. https://bit.ly/3iK8b0g

Lei, H., Li, S., Chiu, M. M., & Lu, M. (2018). Social support and Internet addiction among mainland Chinese teenagers and young adults: A meta-analysis. Computers in Human Behavior, 85, 200-209. https://doi.org/10.1016/j.chb.2018.03.041. https://doi.org/10.1016/j.chb.2018.03.041

Lozano-Blasco, R., Cort'es-Pascual, A., & Latorre-Martínez, M. P. (2020). Being a cybervictim and a cyberbully - The duality of cyberbullying: A meta-analysis. Computers in Human Behavior, 111, 106444. https://doi.org/10.1016/j.chb.2020.106444.

Lozano-Blasco, R.; Cortés-Pascual, A. Problematic Internet uses and depression in adolescents: A meta-Analysis. Comunicar 2020, 28, 109-120. [Google Scholar] [CrossRef].

Holt, M. K., Vivolo-Kantor, A. M., Polanin, J. R., Holland, K. M., DeGue, S., Matjasko, J. L., Wolfe, M., & Reid, G. (2015). Bullying and Suicidal Ideation and Behaviors: A Meta-Analysis. PEDIATRICS, 135(2), e496-e509. https://doi.org/10.1542/peds.2014-1864.

R1.3. We are grateful for suggestions to supplement the theoretical framework: 4 references have been included and briefly illustrated in the introductory sections of the manuscript.

C1.4. Methodology: add a main figure where the procedure and research design are visually explained.

R1.4. We have inserted a new figure (Figure 1 - below) in the revised manuscript which summarizes visually the research methodology and its main steps. We thank the Reviewer for suggesting this.

C1.5. Participants: write the sociodemographic information in a table to make it more visual. Clarify the regions that have been involved in Italy, a heat map would bring innovation and creativity.

R1.5. We have added a table with demographic information (Table 1), including information about geographical distribution. We thank the reviewer for the suggestion of inserting a heatmap for the geographical distribution, but decided not to follow it, to reduce the number of figures and since it would have provided only limited information in addition to what is now presented in Table 1.

C1.6. Instruments: very well developed but do not forget to put the Italian version next to the name. In the references it is fine, but it is important for the scientific community to see that you have used standardized tests adapted to their culture.

R1.6. We have specified that we used the Italian version of the scales used, and included a reference to the study that includes information about the validity of the scales.

C1.7. Statistical analysis: very well developed, do you think that mediation analysis could provide more information on the moderating variables?

R1.7. We thank the reviewer for suggesting this analysis. We have followed the reviewer’s suggestion and conducted additional analyses to investigate mediation and moderation. We have included in the SEM model direct effects for each of the personality traits on moral disengagement as a possible mediator (of their effect on the cyberbullying factors), and formally tested the indirect effects mediated by moral disengagement (the results are discussed in the revised manuscript and the test of all the indirect and total effects are reported in a new table - Table 7). Moreover, to investigate the role of categorical factors as as gender, time on social networks and number of SN profiles as moderators of the (direct and mediated) effects of personality and moral disengagement on cyberbullying, we have conducted multigroup SEM analyses.

C1.8. Results: very interesting but Figure 1 has poor visual quality. The concepts are very good but it looks very blurry and the size of the numbers is very small. The same happens with figure 2. Try to use some application like Canva, Flourish that allows you to improve the quality.

R1.8. We completely agree with the comment of the reviewer about the low quality of the images. However, the files that we uploaded during the submission were all high-resolution, high-quality images (600 dpi), and their quality was downgraded somehow by the submission system that created the pdf of the manuscript. We have inserted here the high-quality version of both figures (one of these figures has been changed in response to a query by the second reviewer and of new analyses conducted. If we will be allowed to submit the revised version, we will try to upload even higher-resolution files.

C1.9. Discussion: I liked it very much, but to give it a plus of quality I recommend that you review more references on the use of social networks and cyberbullying. Add more meta-analysis studies that allow you to give more robustness to your claims. Adding the hypotheses in the text was a great idea. The limitations are very sincere and clear, but I miss two paragraphs on practical implications this research would have.

R1.9. Two new references (Pan et al., 2020; Lozano-Blasco et al., 2022) concerning meta-analysis studies on internet addiction have been added - one of which briefly discussed, to support our claims. We have also added some considerations about practical implications of our research at the end of the discussion.

C1.10. Conclusions: they are great, but you usually don't put references in this section, could you upload those references to discussion and rewrite those sentences in a more general way?

R1.10. We have revised the conclusion, moving the references to the discussion section and re-writing some sentences.

C1.11. Supplementary material: it is very honest that you upload the questionnaire, but it is an opportunity for this to be used by others. I would encourage you to upload the data of your rating along with it, so that it can be used.

R1.11. We are glad that the reviewer appreciated the inclusion of the questionnaire in the supplementary materials. We are not sure to understand what the reviewer would like us to upload, since we already provided the ratings for the cyberbullying items. If the reviewer intended that we should also provide the ratings for the personality and moral disengagement items as well (and not only the aggregated scores), they can be already found in SM2.zip (file SM2_data_descriptives.RData).

Response to Reviewer 2

C2.1. Despite the great efforts, authors are advised to make substantial changes to the manuscript as stated below. Because the review found some limitations regarding methods and results, comments on the discussion section are not addressed.

R2.1. We have revised the manuscript in order to address the issues raised by the reviewer, as indicated in the following points.

C2.2 Methods: Please mention the data collection period and explain the sampling method used

R2.2. We have included in the methods section information about the data collection period and sampling method. We thank the reviewer for pointing out the lack of this information in the original manuscript.

C2.3 Methods: Were the participants in the study all high school students? It seems like adolescents and young adults are mixed as authors included the age range from 14 to 25.

R2.3. Yes, the participants were all high school students. The 19 participants that were in the 21-25 age group were all 21.

C2.4 Methods: Where is the reference to the use of social networks and feelings related to network use questionnaires? Please provide the original reference and how they were modified.

R2.4. There is no reference to these items. Maybe it was not sufficiently clear in the method section that these items were devised ad hoc for this study to measure different facets of social networks use. Actually, all the questions have been reported in the text. They were not meant to provide an aggregate measure of social media use, but as ways to profile participants along some specific characteristics of their use of social networks (e.g. time), in order to investigate differences in cyberbullying across these variables, as it is common to do in similar research [e.g. reference 39]. Concerning the measure about the feelings related to social network use, we had already acknowledged among the limitations of the study the fact that this dimension was not measured with sufficient reliability.

[38] Park S, Na E-Y, Kim E-M. The relationship between online activities, netiquette and cyberbullying. Child Youth Serv Rev. 2014; 42: 74–81. doi: 10.1016/j.childyouth.2014.04.002.

C2.5. Methods: Are the scales used in the survey all well-validated and -translated in Italian? If not validated, how was it applied to the survey? Did the researcher manually translate the items? If the scales are modified from the original version, please indicate both the original and revised version.

R2.5. The scale used for measuring personality was the validated Italian version of the 10 item Personality Inventory [ref 70]. The scales used to measure cyberbullying and moral disengagement about cyberbullying were validated in English [ref 51], and had been translated in Italian by the authors for a previous study, in which Confirmatory Factor Analysis and SEM validated the translation replicating the findings of the study that introduced these scales. [ref 68]

[70] Guido G, Peluso AM, Capestro M, Miglietta M. An Italian version of the 10-item Big Five Inventory: An application to hedonic and utilitarian shopping values. Pers Individ Differ. 2015; 76: 135–140. doi:. 10.1016/j.paid.2014.11.053.

[51] Meter DJ, Bauman S. Moral disengagement about cyberbullying and parental monitoring: Effects on traditional bullying and victimization via cyberbullying involvement. J Early Adolesc. 2018. 38: 303–326. doi: 10.1177/0272431616670752.

[68] Parlangeli O, Marchigiani E, Guidi S, Bracci M, Andreadis A, Zambon R. I Do It Because I Feel that...Moral Disengagement and Emotions in Cyberbullying and Cybervictimisation. In: Meiselwitz G., editor. Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis. HCII 2020. Lecture Notes in Computer Science, vol 12194. Cham: Springer; 2020. pp. 289-304. doi: 10.1007/978-3-030-49570-1_20.

C2.6. Results: It seems like the results of chi-square is only reported in the text, did the authors not create table?

R2.6. We did create the tables for all the chi-square tests conducted, but we omitted them from the manuscript choosing to highlight the main findings (for which groups frequencies differed). (Note: it is not clear from the reviewer’s comment which tables the reviewer had in mind). We could include these tables as supplementary materials of the revised manuscript.

C2.7. Results: Authors attempted to test several hypotheses but having so many hypotheses often mislead the readers to lose focus of the study. To make the results section more reader-friendly, it is recommended for the authors to match the results with the corresponding hypotheses so that they/we could understand why these results are presented in this section.

R2.7. Although we understand that the many results presented in the results section, deriving from a complex study addressing several research questions and testing several hypothesis, might mislead the readers to lose focus of the study, we also believe that the way the results are currently organized is logical and follow the logical order highlighted in the statistical analysis section in order to answer the research questions. Moreover, the discussion section (not addressed by the comments by the reviewer) matches the results to the research questions and hypotheses. We have however edited the results subsections titles in order to make it more evident the link with the research questions and hypotheses that the results in each subsection are functional to address. Also, in response to a suggestion by Reviewer 1 we have included immediately before the Results section a visual summary of the methodology of the study, in which the research questions are put in correspondence with the different types of data analyses conducted.

C2.8. Results: When reviewing the methods section, I thought six items used to measure cyberbullying would be combined as a single variable instead of splitting each item. If the purpose of the current study was to measure the different types of cyberbullying, it was more appropriate to choose a scale consisting of several subscales rather than splitting a single measurement tool by items. This is a big limitation therefore it should be clearly stated in the limitation section.

R2.8. Actually, this was the aim of the study: to verify whether a scale, made up of “only” 6 items, and which is usually used as a tool to measure cyberbullying as a unitary phenomenon, could not instead be revealing of different aspects, different reasons and different manifestations of harmful virtual behavior. To test this hypothesis we tried to relate the personality traits and the mechanisms of Moral Disengagement with each of the items, and again for this reason we conducted the factor analysis.

The results go in the hypothesized direction: for each of the behaviors related to each item, different relationships with personality traits can be highlighted. Factor analysis also suggested the existence of at least two factors.

We hope that in the revised version of our manuscript this will be clearer.

C2.9. Results: I don’t quite understand the intention behind conducting the EFA, CFA, and structural equation modelling in this study. This study did not develop a scale nor did it validate the existing scale. Why should these analyses be performed? For what purpose?

R2.9. The reasons for conducting the analyses highlighted in the reviewer’s comment are first of all related to research question 4, in conjunction with the previous research questions. This research question concerned whether cyberbullying is better conceived and measured as a unitary construct or as a multidimensional construct, and to this aim exploratory factor analysis followed by confirmatory factor analysis is a common analytical strategy. In a way, it can be seen as an attempt to validate the scale by Meter and Bauman, since the items that we used to measure cyberbullying were taken from that scale. The SEM analysis was conducted to test hypotheses about the association between the different latent factors of cyberbullying, personality traits, moral disengagement and other variables. In the revised version of the manuscript we present additional multigroup SEM analyses conducted to better investigate differences across groups and to test hypotheses about mediation (by moral disengagement) and moderation (by gender, time on social networks and number of profiles) on the effect of personality and moral disengagement on the different types of cyberbullying (as measured by the two latent factors extracted by the previous steps - EFA/CFA). We have revised the statistical analysis and results sections of the paper to make it clearer what was the purpose of all these analyses.

C2.10. Results: Figure 2 is very confusing, and it is not a typical arrangement for SEM. Please refer back to other SEM research to redraw the figure. Why is there no latent variable for openness, extraversion etc. and for social network uses? Just by looking at this figure, the DV and IV are unclear.

R2.10. We realize that figure 2 was not a typical display of a SEM model, as we had introduced some mapping between coefficients’ size/sign/significance and visual features (width, color and linetype) to make some patterns more evident. But in the revised manuscript, that includes the results of new analyses conducted in response to a comment by Reviewer 1, we have redrawn the figure (now Figure 3) making it more similar to typical SEM path diagrams. However, we have kept the color coding scheme for the paths representing the direction of the association, and we have also used dashed lines to represent insignificant path coefficients in the fitted model, as we believe it makes it easier to read the effects in complex models like this one.

C2.11: Lastly, the sentences are way too long in some paragraphs. Please polish the language in order to improve the overall quality of the manuscript.

R2.11. We have edited the language to improve readability and overall manuscript quality.

Response to Reviewer 3

C3.1. This paper conducted a questionnaire that was administered to a large sample of high school students to validate the four hypotheses regarding cyberbullying. These research questions are interesting and provide insights about cyberbullying behavior. For example, statistical analyses in this work show that cyberbullying is not a unitary construct but a multidimensional construct.

R3.1. We thank reviewer 3 for appreciating the underlying rationale of our study and the results we obtained

C3.2. I have following suggestions to further improve the manuscript:

1. Given the extreme popularity of AI and Machine Learning, in related work, the authors might consider including recent works that used machine learning models to validate similar findings. For example, in [1], the authors showed that user's personality traits and peer influence are important predictors of cyberbullying. In [2], the authors also showed capturing the repetitive pattern in cyberbullying behavior can improve the performance.

[1] PI-bully: Personalized cyberbullying detection with peer influence

[2]Hierarchical attention networks for cyberbullying detection on the instagram social network

R3.2. In fact, in the previous version of this manuscript there was only one reference related to this important field of research.

Now, in the introduction, 7 more references have been added and discussed, including those suggested by the reviewer.

C3.3. 2. The figures are very low-quality and it is hard to read. The authors need to replace them with high-resolution figures.

R3.3. As we stated in R1.X We agree on the low quality of the figures. However, the files that we uploaded during the submission were all high-resolution, high-quality images (600 dpi), and their quality was downgraded somehow by the submission system that created the pdf of the manuscript. We are sure that in the final version of this manuscript all figures will be high-quality images.

C3.4. 3. The cyberbullying definition is not clear, and sometimes confuses with other similar concept, such as cyber-aggression [3]. The authors need to clarify these differences.

[3]Cyberbullying: Bullying in the digital age

R3.4. In the introduction it has been clarified, also reporting two new references, which is the generally shared definition of cyberbullying and its differences with cyber incivility and cyber aggression

Submitted filename: Response to Reviewers.docx

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10 May 2022

How many cyberbullying(s)? A non-unitary perspective for offensive online behaviours

PONE-D-21-37456R1

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30 May 2022

PONE-D-21-37456R1

How many cyberbullying(s)? A non-unitary perspective for offensive online behaviours

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