Early Adolescents and Exposure to Risks Online: What Is the Role of Parental Mediation Styles? ^†

Abstract

Studies indicate that early adolescents are exposed to several online risks. Furthermore, early adolescents with Special Educational Needs (SENs) often experience emotional, social, or family difficulties, which increase their vulnerability to online risks. We aimed to investigate whether parental mediation styles regarding children’s Internet use moderate the risk for early adolescents in general and early adolescents with SENs in particular. One hundred and nineteen Italian parents (90.8% female) of children aged 11–15, 34% with a diagnosis associated with SENs, completed self-report measures assessing their children’s exposure to online risks and their parental mediation styles. In addition, 70 early adolescents (43.7% female; 39.4% with an SEN diagnosis) completed measures of social adjustment. Using moderation regression analyses, we examined the associations of parental mediation style, social adjustment, and SEN status with exposure to online risks. The findings highlighted how high levels of active parental mediation were associated with a significant reduction in online risks for adolescents with higher social adjustment. Follow-up analyses indicate, even if marginally, that this effect influences regards, in particular, adolescents with SENs. These results highlight the relevance of both individual social adaptation skills and parental mediation in reducing online risks among early adolescents, particularly those with SENs. Therefore, preventive interventions should not only target the development of youth competencies but also provide guidance and support for parents.

1. Introduction

The World Health Organization defines early adolescence as the age between 10 and 14 years, at the beginning of the adolescence developmental stage (10–19 years), characterized as the period of life that follows childhood and precedes adulthood (World Health Organization 2014). The literature consistently suggests that the adolescent years represent a complex age in life, when the person is engaged in building their own identity and is in search of autonomy (Batool and Ghayas 2020), and early adolescence, when the child enters puberty and adolescence and undergoes important changes in their social life (in several school systems, including the Italian one, also transitioning from primary to lower secondary school), can represent an even more sensitive age. In today’s digital society, early adolescents are spending increasing amounts of time online, engaging in activities that include education, entertainment, and social interaction (Chen and Fu 2009; Liu et al. 2022a). However, this increased connectivity has introduced a parallel rise in digital risks, including cyberbullying, online grooming, exposure to inappropriate content, and excessive or problematic Internet use (Davidson and Martellozzo 2013; Salgado et al. 2014). These risks can have adverse consequences on adolescents’ psychological well-being, social development, and academic functioning (Floros et al. 2013; Gómez et al. 2017; Liu et al. 2022b).

While a large body of research has focused on vulnerable populations, such as adolescents with Special Educational Needs (SENs) (Al-Yagon and Margalit 2013; El-Asam et al. 2023; Kavale and Forness 1996; Wilson et al. 2009), it is important to recognize that all adolescents face substantial challenges in navigating online environments.

Studies consistently show that typical adolescents are also at risk for cyber-victimization (Tran et al. 2023), risky self-disclosure (Towner et al. 2022), and problematic patterns of Internet use (Pontes and Macur 2021), with prevalence rates that make online risks a widespread developmental concern rather than one limited to clinical or special populations (Dadi et al. 2024). Factors such as low self-esteem, high social anxiety, peer rejection, and poor emotion regulation have been linked to a greater susceptibility to online harm among typically developing adolescents (Andrade et al. 2021; van Dijk et al. 2021). Conversely, protective factors such as strong parent–child communication, digital literacy, and supportive peer relationships have been found to reduce exposure to digital risks (Lukavská et al. 2022).

Moreover, we cannot forget how the transition into adolescence itself is a period of heightened vulnerability, as youth explore independence online while still lacking fully developed executive functioning skills to assess risks and consequences (West et al. 2025). Thus, even in the absence of specific vulnerabilities (such as having learning or emotional difficulties), typical adolescents remain highly exposed to online risks and require effective support systems.

If all adolescents can be exposed to online risks and their negative outcomes, some adolescents, however, can be even more vulnerable than others based on individual, familial, and contextual factors (Ruckwongpatr et al. 2022; Zhu et al. 2021).

In particular, one of these vulnerable populations is adolescents with Special Educational Needs (SENs), which constitutes a heterogeneous group including youth with learning disorders (e.g., dyslexia), attention difficulties (e.g., ADHD), emotional and behavioral problems, and other neurodevelopmental challenges. While SENs are not a clinical diagnosis per se, they signals the need for additional educational, social, or emotional support (UNESCO 2020).

When it comes to online risks, prior studies have linked ADHD and learning disabilities to an increased involvement in cyberbullying, cyber-victimization, and problematic Internet use (Cakmak and Gul 2018; Choi et al. 2019; Eden and Tal 2024; Jackson et al. 2025; Munno et al. 2017). Furthermore, research consistently shows that adolescents with SENs experience higher rates of emotional dysregulation, poor social competence, and difficulty with peer relationships, all of which may impair their ability to navigate online environments safely (Al-Yagon and Margalit 2013; El-Asam et al. 2023; Kavale and Forness 1996; Wilson et al. 2009).

From this perspective, individual characteristics related to social and emotional adjustment can favor or hinder the exposure to online risks in adolescence. Emotional difficulties (e.g., anxiety, depression, and low body satisfaction), which are common among adolescents with and without SENs, have been found to predict a greater susceptibility to online harm, particularly among adolescents with low self-esteem and social withdrawal (El-Asam et al. 2022; Frisén and Berne 2020; Gioia et al. 2021; Landoll et al. 2015). Adolescents who struggle with social adjustment (because of SENs or because of the development-related struggles discussed above) may be missing the interpersonal strategies needed to interpret social cues online or seek help when faced with distressing content or interactions. Moreover, in the case of adolescents with SENs, research indicates that, in addition to cognitive and emotional vulnerabilities, adolescents with SENs often experience less adult supervision and social inclusion, which can exacerbate their online risk (Maheux et al. 2025).

Within this framework, parental mediation has emerged as a critical protective factor against the exposure to online risks in adolescence. Parental mediation includes a spectrum of behaviors aimed at managing children’s media consumption, ranging from restrictive strategies (e.g., setting limits or blocking content) to active strategies (e.g., engaging in open discussions about online experiences) (Padilla-Walker et al. 2012; Livingstone et al. 2011). While both forms of mediation can influence digital behaviors, active mediation has been associated with more constructive Internet use, improved digital literacy, and a lower exposure to online risks (Gómez et al. 2017; Kalmus et al. 2022; Liu et al. 2023; Ren and Zhu 2022). Interestingly, the effectiveness of parental mediation may be contingent on adolescents’ individual characteristics. For example, children with better emotional regulation or social skills may benefit more from discussions about online behavior, whereas children with SENs may require additional support or more intensive monitoring (Del Río et al. 2019).

Taken together, these findings emphasize the importance of studying both adolescents with SENs and their peers without SENs within the same framework. Although the mechanisms of vulnerability may differ, the role of parental mediation emerges as crucial for all youth.

Despite the growing literature on parental mediation and digital risks, how parental mediation might interact with children’s social–emotional functioning—such as social adjustment—in determining adolescents’ vulnerability to online risks, among both adolescents with and without SENs, is still understudied. Furthermore, the existing research on this topic is scarce in the Italian context, and most studies have not used moderation frameworks to analyze these complex interactions. Understanding how individual and family-level protective factors intersect is essential for designing prevention efforts that are developmentally and contextually appropriate.

The Current Study

As discussed above, the existing literature has highlighted the increased vulnerability of youth, especially youth with SENs, in online environments, as well as suggesting the potential protective role of specific parental mediation strategies (Del Río et al. 2019). However, little is known about how these dynamics apply specifically to Italian culture. In particular, more data is needed to have a more in depth understanding of types of online risks to which early adolescents can be exposed and how parental mediation interacts with the individual characteristics of early adolescents (e.g., social or school adjustment) in influencing exposure to online risks.

This study aimed to address these gaps by examining whether parental mediation styles, particularly active mediation, moderate the association between early adolescents’ characteristics, such as their social adaptation, and their exposure to online risks. The study also explored if this effect varied depending on SEN status.

Based on previous findings (El-Asam et al. 2023; Ybarra et al. 2006), we formulated the following primary hypothesis:

1.

Levels of parental mediation strategies moderate the associations between early adolescents’ characteristics related to psychological and social adjustment (possible protective or risk factors) and their exposure to online risks. That is, we hypothesized that the associations between early adolescents’ characteristics related to psychological and social adjustment (possible protective or risk factors) and their exposure to online risks vary as a function of the levels of parental mediation strategies.

Furthermore, we examined the following secondary hypotheses (declinations of the primary hypothesis):

2.

The moderation by parental mediation of the associations between individual characteristics and online risks varies depending on the type of mediation strategies, with high levels of active mediation being a stronger protective factor (Alheneidi et al. 2021; Fernandes et al. 2020);

3.

The moderation by parental mediation of the associations between individual characteristics and online risks also varies depending on the type of online risks to which early adolescents are exposed;

4.

The moderation by parental mediation of the associations between individual characteristics and online risks varies for early adolescents with SENs and without SENs.

We tested these hypotheses using data from 119 parents and 70 of their early adolescent children (aged 11–15). As preliminary analyses necessary for testing hypotheses 2 and 3, we explored the existence of different dimensions of parental mediation and of different types of online risks to which early adolescents are exposed by running factor analyses. Then, hypotheses 1, 2, and 3 were examined by constructing performing moderation regression models where different individual characteristics were examined as possible predictors of the exposure to online risks (criterion variables), and the parental mediation strategies were introduced as possible moderators (Figure 1).

Hypothesis 4 was, then, tested by running the moderation regression models for which the moderation by the parental mediation strategies was significant separately among adolescents with SENs and among adolescents without SENs.

By exploring these questions, the study aims to clarify the joint roles of parental mediation and individual social–emotional functioning in shaping adolescents’ online risk profiles.

2. Methods

The study received ethical approval from the Ethical Committee of the Psychology Department of Catholic University of the Sacred Heart (approval received in September 2020, approval number: 88/25), as well as authorization from the Boards of Directors of the participating psychoeducational centers.

Six psychoeducational centers and two middle schools (grades 6–8) in Northern Italy participated in the study. The two participating schools distributed study information materials and two separate informed consent forms—one for parental participation and one for child participation—to families. Parents who consented to participate received links via email to complete the self-report questionnaires—one for themselves and one for their child. In collaboration with school personnel, online sessions were arranged to allow children to complete their questionnaires under the supervision of a researcher. Each child’s and parent’s responses were linked using an anonymous code to maintain confidentiality.

Simultaneously, the study was promoted across participating centers throughout Italy. Psychologists coordinating these centers were trained in the study protocol and in administering the questionnaires to both children and parents.

2.1. Measures

2.1.1. Parent Report Measures

• This study was part of a larger project on the online experiences of early adolescents and parental skills and behaviors towards their children’s Internet use. Therefore, parents responded to a battery of measures including a set of ad hoc items developed to assess their child’s typical Internet use (e.g., “To your knowledge, which social networks or platforms does your son/daughter use the most?”), questions on their own digital skills in comparison to their child’s (e.g., “Do you (parent) have at least one profile on a social network?”), questions from the checklist developed for the Net Children Go Mobile project (Mascheroni and Ólafsson 2014), and validated questionnaires. For the current study, we considered only the following variables: parental mediation strategies, their children’s exposure to online risks and their children’s psychological adjustment.
• Parental mediation strategies: Net Children Go Mobile checklist, Parent Form Q (Mascheroni and Ólafsson 2014).
  Parental mediation strategies were assessed using the checklist from the Net Children Go Mobile project (Mascheroni and Ólafsson 2014), Parent Form Q. The scale measures mediation, monitoring, and parental concerns on a three-point scale. Specifically, parents could answer choosing the options “never” (coded as 0), “sometimes” (coded as 1), and “often” (coded as 2) to answer questions such as, “How often do you do the following activities with your son/daughter: talk with him/her about what you can and cannot do on the internet, stay close to him/her when he/she uses the internet”. For the current study, we only considered the subscale for mediation consisting of 13 items assessing both restrictive and active forms of mediation (e.g., setting rules, discussing online content).
• Children’s exposure to online risks: Net Children Go Mobile checklist (Mascheroni and Ólafsson 2014).
  The experience of children in terms of exposure to the online risks were assessed by asking parents to answer six items from the Net Children Go Mobile project (Mascheroni and Ólafsson 2014). Parents answered how many times in the past year different situations had happened to their child, with three response options: “often,” “only 1 or 2 times,” or “never.” The items are reported in Table 2. For the analyses, the scores of the items were dichotomized (1 = “often” or “only 1 or 2 times”; 0 = “never”). This recoding was implemented for several reasons. First, the distribution of responses was highly skewed, with relatively few parents endorsing the “often” category, leading to sparse data in that cell and reducing statistical power. Combining “often” and “only 1 or 2 times” into a single category indicating any exposure allowed for a more robust estimation of associations. Second, from a conceptual standpoint, any experience of the risk, regardless of frequency, can be considered meaningful and relevant to children’s safety and well-being. Dichotomization therefore facilitated the interpretation of results as reflecting the presence versus absence of exposure.
• Adolescents’ psychological adjustment: Strengths and Difficulties Questionnaire—SDQ (Goodman 1997).
  The SDQ (Goodman 1997) is a widely used behavioral screening tool that assesses psychological adjustment in children and adolescents. The parent report version includes 25 items covering five subscales: Emotional Symptoms, Conduct Problems, Hyperactivity/Inattention, Peer Problems, and Prosocial Behavior. Each item is rated on a three-point scale (0 = not true, 1 = somewhat true, 2 = certainly true). A Total Difficulties score is computed by summing the first four subscales. The SDQ has demonstrated good reliability and validity across diverse populations. In the current study, the internal consistency of Cronbach’s Alpha ranged from 0.69 to 0.79 for the subscales of the SDQ, except for the subscale of Peer Problems (Cronbach’s Alpha = 0.53).

2.1.2. Adolescent Report Measures

Like their parents, adolescents participating in this study responded to a battery including several measures to investigate their experience with the Internet and their social behavior. For the current study, the following variables were considered: indicators of school success, in terms of school and social adjustment; body identity; emotionality; and family relationships.

• Indicators of school success: Analysis of Cognitive-Emotional Indicators of School Success (ACESS).
  The ACESS (Vermigli 2002) is a self-report tool designed to assess key emotional and cognitive factors related to school adjustment. It includes subscales on Emotionality (e.g., anxiety, sadness), Body Identity (self-perception and body satisfaction), School Adjustment (academic engagement and satisfaction), Social Adjustment (experiencing good peer relationships and social integration), and Family Relationships (experiencing the family as a point of reference from which they can obtain the necessary support to face new experiences). Items are rated using a four-point Likert scale: from “absolutely false” (1) to “absolutely true” (4). This instrument is particularly suitable for use with early adolescents and has been employed in educational and psychological assessments in Italy. The subscale scores have been calculated as the average of the item scores. Cronbach’s Alpha showed good levels of reliability of the five subscales. α = 0.89 for School Adjustment, α = 0.88 Emotionality, α = 0.69 for Body Identity, α = 0.76 for Social Adjustment, and α = 0.86 for Family Relationships.

2.2. Participants

A total of 119 parents (90.8% female, only one parent per child), with children aged 11 to 15 years (M: 12.97; SD: 1.47), participated in the study. Among the participating parents, 66% reported having a child without a diagnosis, while 34% reported a diagnosis in their child(ren): 64% of the children had specific learning disabilities (SLDs), 17.5% had attention-deficit/hyperactivity disorder (ADHD), 11% had emotional difficulties (ED) (anxiety disorders, anger management difficulties), 5% had obsessive–compulsive disorder (OCD), and 2.5% had conduct disorders (CDs). Characteristics of the children of the parents participating in the study are reported in

Table 1. Characteristics of children whose parents participated.

Not SEN		SEN
F	M	F	M
34	39	18	28
M Age (SD)		M Age (SD)
12.82 (1.3)	12.56 (1.5)	13.17 (1.3)	12.54 (1.4)

. Parents of children with Special Educational Needs came from psychological centers for the most part (n = 27), while others (n = 14) were part of the sample from middle schools. All 78 children without SENs were from middle schools.

Besides the parents, 70 of their children (56.3% male) received parental approval to participate in the study and answered the battery of the adolescent measures. Of these participating adolescents, 60.6% were without a diagnosis of SEN, and 39.4% had a diagnosis. Among the diagnoses of the participating children, 82.2% were diagnosed with SLD, 14% with ADHD, and a smaller percentage with obsessive–compulsive disorder (OCD).

Almost all participants (89.9% of parents and 93.3% of children) were born in Italy, and there were no significant differences in the frequency of children with SENs within the two gender groups (42% of males and 35% of females had SENs), t (117) = 2.53, p = 0.430.

2.3. Strategy of Analysis

Data were cross-sectional and were analyzed using SPSS (version 25) and PROCESS version 3.3 and Mplus version 8.11 (Muthén and Muthén 1998–2017).

As a preliminary step to test hypothesis 2, a factor analysis (extraction method: Principal Component Analysis, rotation: Oblimin with Kaiser normalization) was run on the mediation subscale from the Parent Form Q of the Net Children Go Mobile project checklist (Mascheroni and Ólafsson 2014). An oblique rotation (Oblimin) was selected to allow for potential correlations between different parental mediation strategies, consistent with theoretical models suggesting that restrictive and active approaches may co-occur within the same families. Only factors with eigenvalues higher than 1 were considered. The scale structure that emerged was then confirmed by performing a Confirmatory Factor Analysis (Maximum Likelihood estimator; Muthén and Muthén 1998–2017). To evaluate the goodness of fit of the model, we considered the value of the Chi-square (χ2) index of the model, with acceptable models obtaining a non-significant Chi-square value. Because of the Chi-square index sensitivity to the size of the sample (becoming significant for large samples), we also examined the CFI index, with a value >0.90 for an acceptable fit (Bollen 1989), and the RMSEA index, with a value <0.08 for an acceptable fit.

As a preliminary step of analysis needed to test hypothesis 3, we explored the dimensionality of the checklist on online risks completed by parents in order to identify different categories of exposure to online risks. To this aim, we ran a factor analysis on the dichotomized items from the checklist, using Principal Component Analysis as the extraction method, with Varimax rotation and Kaiser normalization. Factors with eigenvalues greater than 1 were retained.

Then, to test hypotheses 1, 2, and 3, we ran a series of moderation regression models (PROCESS 3.3.; model 1) to examine whether parental mediation strategies moderated the relationships between individual characteristics and online risk exposure. That is, according to Hayes’ model of moderation (Hayes 2022), in each model, we examined whether the effect of the predictor (the independent variable) on the criterion variable varied as a function of the value of the parental mediation strategies (the moderator variable). For the models in which the moderation/interaction term of the predictor variable*parental mediation strategies was significantly associated with the criterion variable, as a follow-up analysis, we examined which value the coefficient of the association between the predictor and the criterion variables assumed for high (+1 SD), middle (SD = 0), and low (−1 SD) levels of parental mediation strategies (the moderator variable).

Each model included one type of online risk exposure as a criterion variable, one of the child-related characteristics measured with ACESS and SDQ questionnaire subscales as a predictor, and one of the dimensions of the mediation strategies found in the factor analysis as a moderator. In all the models, the “SEN status” was included as a covariate. An overview of the variables used in the regression models is displayed in

Table 2. Variables used in the moderation regression models.

Variables	Measure (Source)	Role in the Moderation Regression Models
Individual Characteristics Family Relationship Social Adjustment School Adjustment Emotionality Body Identity	ACESS (adolescents)	Predictor variables: each variable in separate models
Emotional Problems Problematic Behavior Hyperactivity and Inattention Problematic Relation with Peers Prosocial Behavior	SDQ (parents)	Predictor variables: each variable in separate models
Parental Mediation Strategies—Dimensions (Hypothesis 2) Control Active Mediation and Co-Use	Net Children Go Mobile checklist, Parent Form Q (parents)	Moderation variables (hypothesis 1): each variable in separate models
Online risks Contact Risks (Hypothesis 3) Conduct Risks (Hypothesis 3) Online Risks Total Score	Net Children Go Mobile checklist—six items (parents)	Criterion variables: each variable in separate models

.

All the models that were run contributed to testing the general hypothesis 1 (Figure 1). The models in which the different dimensions of parental mediation strategies (emerged in the factor analysis) were the moderator variables allowed for testing hypothesis 2, while the models in which the different types of risks online that emerged in the factor analysis were the criterion variables allowed for testing hypothesis 3.

Lastly, to examine hypothesis 4, the moderation regression models for which the moderation by parental mediation strategies emerged as being significant were run separately among adolescents with SENs and adolescents without SENs.

3. Results

Data on online risks and vulnerability characteristics were collected from both children and parents. Children completed the ACESS questionnaire, while parents completed the SDQ. All analyses were performed in SPSS using PROCESS version 3.3.

3.1. Factor Analyses

3.1.1. Factor Analysis of the Parental Mediation Strategies

Two main dimensions of parental mediation (

Table 3. Principal component analysis of parental mediation strategies: rotated component matrix.

	Control	Active Mediation and CO-Use
I check the material downloaded from the Internet	0.851	0.315
I check his chats	0.850	0.098
I check his social profiles	0.828	0.119
I check the friends he interacts with online	0.803	0.170
Control the apps he downloads on his phone	0.698	0.049
Limit his time on the Internet	0.687	−0.031
Controlling his search history	0.683	0.562
I use a parental control filter	0.510	0.151
I talk to him about what he can and cannot do online	0.262	0.705
I talk to him about modified photos of models	−0.112	0.607
I stay close to him when he is on the Internet	0.360	0.602
I encourage him to learn new things on the Internet	−0.059	0.555
Share time in online activities with him/her	0.314	0.529

Note. Bold: Main loadings of the items in the factors.

) emerged from the exploratory factor analysis run on the parental mediation strategies subscale (preliminary analysis for the test of hypothesis 2): parental control, expressing more restrictive mediation strategies and explaining 37.9% of the total variance, and active mediation, expressing more co-use and dialogue strategies and explaining 14.1% of the total variance. In the Confirmatory Factor Analysis, after leaving four pairs of measurement errors free to correlate, the two-factor model fitted the data adequately: Chi-square(60) = 101.549, p = 0.001; CFI = 0.931; RMSEA = 0.076. We used the two-factor scores in the following analyses.

3.1.2. Factor Analysis of the Online Risk Checklist

The exploratory factor analysis that was run on the online risk checklist (preliminary analysis for hypothesis 3) resulted in the identification of two dimensions: Contact Risks (risks suffered) and Conduct Risks (risks acted upon) (see

Table 4. Principal component analysis of the checklist for the online risks: rotated component matrix.

	Contact Risks	Conduct Risks
He/she has been excluded from chats	0.791	0.091
Has been offended in a chat room or social network	0.789	−0.003
Someone he/she met online asked to meet him/her	0.587	0.184
Pretended to be someone else	0.205	0.732
Made fun of someone in a chat room or social network	0.389	0.709
Spread offensive images in a chat room or social network	0.19	0.537

). Contact Risks accounted for 34.32% of the total variance, while Conduct Risks accounted for 18.85%. Based on these results, we derived three outcome scores for use as dependent variables in the moderation models: (1) the factor score for Contact Risks, (2) the factor score for Conduct Risks, and (3) the sum of these two factor scores to create a total index of online risks.

3.2. Moderation Regression Analyses

3.2.1. Moderation Models Run on All the Adolescents (Hypotheses 1, 2, and 3)

In order to test hypotheses 1, 2, and 3, we ran a series of moderation regression models among all the adolescents of the sample, each model with one of the individual characteristics as a predictor and active mediation or parental control as the moderating variable (to test hypothesis 2), and one of the three scores of the online risk exposure as the criterion variable (Contact Risks, Conduct Risks, Total Risks) to test hypothesis 3. All the models also included the SEN condition as a covariate.

The only model in which the parental mediation strategies significantly moderated the relationship between child characteristics and the exposure to risks online was the model including the level of social adjustment as a predictor and active mediation as a moderator. In the model, 27% of the variance of the exposure to online risks was explained by the predictors (R² = 0.266, p = 0.001). Social adjustment was not significantly associated with online risks (b = −0.06; p = 0.248), but active mediation was (b = 0.69; p = 0.003), and the SEN condition was as well (b = 1.08; p = 0.039), showing a higher exposure to the online risks for SEN participants. The interaction term social adjustment*active mediation was significantly and negatively associated with the exposure to online risks: b = −0.16; p = 0.013.

The follow up analyses (Figure 2) showed that, when the levels of active mediation were high (+1 SD), higher levels of the child’s social adjustment were associated significantly with a lower exposure to the online risks (b = −0.231, p = 0.017), whereas the adolescent’s social adjustment was not associated significantly with the online risks for average and low (−1 SD) levels of active mediation (average active mediation, b = −0.066, p = 0.247; low active mediation, b = 0.100, p = 0.198).

3.2.2. Adolescents with and Without SENs: Separate Moderation Regression Models

Then, in order to investigate hypothesis 4, we ran the moderation model with social adjustment as the independent variable, active mediation as the moderator, and the exposure to online risks as the criterion variable separately among participants with SENs and participants without SENs. Among the adolescents without SENs, all the predictors (social adjustment, active mediation, and the interaction term) were not significantly associated with the exposure to the online risks. The coefficients are reported in

Table 5. Moderation model among adolescents without SENs.

			95% Confidence Interval
	Estimate	SE	Lower	Upper	Z	p
Social Adjustment	−0.113	0.0996	−0.308	0.0821	−1.14	0.256
Active Mediation	0.364	0.2851	−0.194	0.9231	1.28	0.201
Social Adj. ∗ Active Med.	−0.134	0.0946	−0.320	0.0509	−1.42	0.155

.

Among adolescents with SENs, parental active mediation was significantly and positively associated with the exposure to the online risks, indicating that, in the presence of higher levels of online risks, parents adopted higher levels of active mediation when their children had SENs, and the interaction term was marginally significant (p = 0.076). The results are reported in

Table 6. Moderation model among adolescents with SENs.

			95% Confidence Interval
	Estimate	SE	Lower	Upper	Z	p
Social Adjustment	−0.0763	0.0685	−0.211	0.0579	−1.11	0.265
Active Mediation	1.1398	0.3209	0.511	1.7687	3.55	<0.001
Social Adj. ∗ Active Med.	−0.1623	0.0914	−0.341	0.0168	−1.78	0.076

.

Follow-up analyses for low (−1 SD), middle, and high (+1 SD) levels of active mediation indicated that, only for high levels of active mediation, individual social adjustment was marginally (p = 0.078) and negatively (b = −1.763) associated with the online risks, confirming the tendency of being less exposed to online risks when the adolescent with SENs was more highly socially adjusted and experienced more active mediation by parents.

4. Discussion

This study aimed at investigating whether and how parental mediation strategies interact with adolescents’ social adjustment in shaping exposure to online risks. Specifically, we hypothesized that parental mediation can be a protective factor by moderating the associations between individual characteristics of emotional and social adjustment and online risks (hypothesis 1), and that this moderation can vary depending on the category of online risks (hypothesis 2), the type of parental mediation strategies (hypothesis 3), and whether early adolescents have a SENs diagnosis or not (hypothesis 4). Drawing on the existing literature that highlights both the vulnerability of early adolescents, especially early adolescents with SENs, in digital contexts (del Río et al. 2019) and the protective role of family mediation strategies (Padilla-Walker et al. 2012; Gómez et al. 2017; Ren and Zhu 2022; Liu et al. 2023), our findings offer a more nuanced understanding of how these factors jointly shape adolescents’ online risk experiences.

In accordance with our hypotheses 1 and 2, the central finding of the study was the moderating role of active parental mediation among all early adolescents (also controlling for the effect of the SEN condition). Adolescents with higher levels of social adjustment experienced fewer online risks (all types), but only when active parental mediation was also high. Instead, parental mediation strategies, mainly expressed through the control of children’s online activities, did not buffer any of the associations between individual characteristics and the exposure to online risks.

It is worth considering whether parental active mediation of Internet use is part of a broader parental strategy that extends beyond the digital environment. Indeed, some studies have shown that active mediation is not only associated with lower online risks but also with reduced offline risks, such as early sexual activity and substance use (Collier et al. 2016).

More restrictive types of parental mediation did not have the same protective effect, indicating that mediation by parents is effective when consisting of dialogue and more open communication with the child about their activities online and co-use with the child.

Previous studies examining the family context generally agree that open parent–child communication and a positive parent–child relationship are associated with a lower exposure to online risks (Barlett and Fennel 2018; Bleakley et al. 2016; Byrne et al. 2014; Elsaessera et al. 2017). Being well-informed about children’s online activities appears to be an important component of effective mediation strategies, as is possessing good digital skills (Kalmus et al. 2015).

Conversely, adolescents tend to disclose less about the difficulties they encounter online if they perceive their parents as digitally incompetent or primarily focused on interrupting their activities and connections (Baldry et al. 2019).

Considering the protective role of this strategy, it may be important for parent support programs, including those designed for parents of SEN children, to include ways of teaching and fostering active mediation skills. Such programs could also encourage parents to adopt a more positive and balanced view of the Internet, focusing not only on potential risks but also on opportunities and emphasizing the importance of sharing information and reflections about the digital world with their children. The observed positive association between active parental mediation and online risk exposure among adolescents with SENs could suggest that such strategies were implemented in response to the detection of prior online risks. This highlights the possibility that it may be important to promote active mediation proactively, before children and early adolescents are exposed to potential online risks.

Interestingly, however, hypothesis 3 was not confirmed. Indeed, the moderation effect enacted by the parental mediation strategies was found only when the total score of the exposure to online risks was the criterion variable, and not for the two types of risks online that emerged in the factor analysis. This result indicates that using parental active mediation strategies is a protective factor in general, independently of the category of the online risks (Contact or Conduct Risks), because, under the condition of experiencing an emotional supportive family context, early adolescents who have good social skills and experience good relationships with peers are less exposed to all types of online risks.

This effect was confirmed (even if only marginally) among adolescents with SENs, supporting hypothesis 4. Even if this finding needs to be interpreted cautiously, since it only shows a tendency to statistical significance, it suggests that active parental involvement may be particularly protective for youth with pre-existing vulnerabilities. This finding extends the previous research by providing some evidence that the benefits of active mediation are not uniform but depend on the adolescent’s individual social–emotional profile (Livingstone et al. 2011). Among adolescents without SENs, neither the social adjustment nor the active mediation and the interaction term were significantly associated with the exposure to online risks. This result needs to be discussed taking into consideration the limited size of the adolescent sample participating in this study. Indeed, it is likely that the marginal effect found among the adolescents with SENs would become significant in a larger sample, and that some of the non-significant results that emerged among the adolescents without SENs might become significant. We need further research using larger samples to confirm our results. We cannot, however, exclude the possibility that other dimensions can be stronger protective factors than parental active mediation for the adolescent without SENs.

Taken together, the results from this study lead us to consider interpreting and treating online risk not solely as a function of time spent online but as the product of a more complex interaction between individual adjustment and family dynamics. This in turn would lead to an ecological approach to digital safety, where risk is shaped by both internal capacities (e.g., emotional regulation, social competence) and external support (e.g., parental engagement).

Notwithstanding the novelty of the results, this study has some limitations. First, the sample was limited in size and geographically focused, which may restrict generalizability. The limited size of the sample might have also affected the strengths of our results, which need to be confirmed and more deeply explored in larger samples. Moreover, the cross-sectional design does not allow for causal conclusions about the direction of observed relationships. Lastly, the discrepancy between parent and child reports also points to the need for multi-informant and longitudinal designs in future research.

5. Conclusions

The findings from this study suggest the importance of considering both individual characteristics and parental mediation styles when addressing young adolescents’ exposure to online risks. We need to empower active mediation strategies and adolescents’ social skills, especially related to peers, in order to prevent or reduce the exposure to online risks. Moreover, while adolescents with SENs are generally more vulnerable in digital environments, our results indicate that active parental mediation can reduce these risks, especially when adolescents exhibit stronger social adjustment skills.

These results indicate that prevention efforts should not only aim to strengthen youth competencies (such as emotional regulation and social functioning) but also support parents in developing effective communication strategies about Internet use. In particular, we need intervention actions aimed to empower, among parents, the adoption of active mediation strategies based on dialogue with the child and the encouragement to learn on the Internet, more than the adoption of restrictive strategies. Such tailored interventions, which combine digital education for adolescents with parent-focused guidance on active mediation, would be useful for all families with early adolescent children, and they might be particularly beneficial for families of children with SENs.

Future studies should investigate how interventions that target both youth social–emotional development and parental mediation strategies can be implemented effectively in educational and clinical settings. In particular, efforts should be made to include families from diverse socio-cultural backgrounds, considering that, in different cultural contexts, different parental strategies, more oriented to active mediation or restrictive practices, can be normative. Furthermore, even if in this study the category of online risk was not relevant, with the fast development of technologies, the digital risk also evolves over time, and new types of risky situations or behaviors can appear This factor needs to be considered and examined further in future research aimed to empower interventions.