Submitted:
26 April 2026
Posted:
28 April 2026
You are already at the latest version
Abstract
Background: Following Australia’s implementation of a nationwide ban on social media access for individuals under 16 years of age in December 2025, several countries have introduced legislative measures mandating age verification on social media platforms as a strategy to mitigate online harms. However, literature examining public opinion toward social media bans for children remains limited.Aim: Within this context, the present study aimed to investigate public opinion regarding the implementation of a social media ban for children, as well as the factors influencing these views.Methods: We conducted a cross-sectional study in Greece in April 2026. Sociodemographic characteristics (including sex, age, presence of children under 18 years of age, educational level, and financial status), patterns of social media use (number of social media accounts, daily time spent on social media, and frequency of posting), and indicators of political engagement (frequency of following political news and frequency of discussing political issues with others) were examined as potential predictors of participants’ opinion toward a social media ban for children. The outcome measures included participants’ level of agreement with the social media ban, level of information regarding its implementation, perceived need for additional measures, confidence in the effectiveness of the ban, perceived impact of the ban on children’s lives, and parental familiarity with digital parental control tools. Multivariable regression analyses were performed to assess associations between predictor variables and study outcomes while controlling for potential confounding factors.Results: In our sample, 69.2% agreed with the implementation of a social media ban for all children under age of 15. Also, most of our sample (92.7%) reported that they need more information from the government regarding the implementation of the ban. Additionally, 86.5% considered that additional measures, beyond a social media ban, should be implemented to address the problem. These measures included digital literacy courses in schools (85.8%), active parental involvement in digital literacy (77.5%), prohibition of inappropriate content (76.1%), reasonable parental limits on social media use (72.1%), and restriction of addictive platform features (69.3%). We found that increased age is associated with increased confidence in the effectiveness of the social media ban. Moreover, being female and having a higher level of education were associated with more positive perceptions regarding the impact of a social media ban on children’s lives. Moreover, our results showed a positive association between age, financial status, total number of accounts on social media, daily time spent on social media, and impact of the social media ban on children’s lives. We found that reduced age is associated with increased parental familiarity with digital parental control tools. Additionally, multivariable analysis identified a positive association between total number of accounts on social media and parental familiarity with digital parental control tools.Conclusions: There is a need for holistic and evidence‑informed policy frameworks that integrate regulatory measures, educational initiatives, and shared accountability among stakeholders. Policymakers should capitalize on existing public support for child protection while simultaneously investing in digital literacy programs, strengthening parental empowerment, and enhancing the regulatory oversight of social media platforms to ensure sustainable and equitable outcomes. In light of the limitations of the present study and the relative scarcity of empirical research in this area, there is an urgent need for further well‑designed and methodologically robust studies to inform effective policy development.
Keywords:
social media
; ban
; children
; public
; opinion
; confidence
; policy
; government
; effectiveness
Introduction
The widespread adoption of smartphones among children has become a defining characteristic of contemporary childhood. In Organisation for Economic Co-operation and Development (OECD) countries, approximately seven out of ten children in the fourth grade, at around 10 years of age, already own a personal smartphone, with ownership reaching nearly universal levels in several northern European countries, largely independent of socio-economic background [1]. As children grow older, smartphone ownership becomes almost ubiquitous: on average, 98% of 15-year-olds across OECD countries report possessing their own device [2]. This high level of access implies that digital technologies are deeply embedded in children’s daily lives. Smartphones are used for multiple purposes, including communication, education, entertainment, and information-seeking. However, social media engagement represents their predominant use. Indeed, approximately 96% of children aged 15 years report browsing social networking platforms [2]. The majority use social media primarily to communicate and share digital content with peers, while a substantial proportion actively participates in content creation or editing.
In addition to ownership and overall usage, patterns of social media use during school hours raise further concerns. Nearly half of 15-year-olds across OECD countries routinely leave notifications from their social media accounts enabled on their smartphones while attending classes, indicating persistent digital connectivity during instructional time [1]. Beyond school settings, children’s engagement with smartphones and social media is substantial during leisure periods. On average, children spend approximately 1.1 hours per day using social media at school, 2.6 hours before and after school, and nearly four hours per day during weekends. For instance, a majority of 15-year-olds in OECD countries (60%) report spending more than two hours per day on social media during weekdays. Also, pronounced cross-national variation is observed, with the proportion ranging from as low as 24% in Japan to more than 80% in Estonia [1].
This extensive and early exposure to social media has raised increasing concerns regarding its potential implications for children’s psychosocial development, wellbeing, and safety. The intensity and pervasiveness of children’s engagement with social media have increasingly been associated with a range of adverse developmental, educational, and mental health outcomes [1,3,4,5]. Evidence from OECD and PISA-related studies indicates that excessive and unregulated use of digital devices among children and adolescents is linked to reduced concentration, poorer academic performance, sleep disturbances, and increased exposure to cyberbullying and inappropriate content [2,6]. Beyond educational effects, particular concern has been raised regarding adolescent mental health, a developmental phase marked by heightened emotional and social vulnerability. Literature suggests associations between intensive social media use and elevated levels of anxiety, depressive symptoms, emotional distress, and lower subjective well-being, especially among heavy users [3,4,7,8]. Moreover, design features such as continuous notifications, algorithm-driven content, and social comparison mechanisms may contribute to compulsive use patterns and reduced opportunities for psychological recovery and offline social interaction [1,5]. For instance, a recent review revealed that research on social media addiction has drawn upon 25 different theories or conceptual models across 55 empirical studies, aiming to elucidate the theoretical perspectives and key constructs proposed to account for the emergence and progression of social media addiction [9]. Furthermore, an increasing body of structural and functional magnetic resonance imaging (MRI) research has focused on compulsive forms of social media use. According to a recent systematic review findings, these studies show partial alignment with neurobiological patterns identified in substance addiction research, suggesting preliminary -but not conclusive- support for the addictive potential of social media use [10].
In response to these concerns, international organizations and policymakers have begun to reassess existing digital governance frameworks, increasingly considering age-based restrictions, enhanced platform accountability, and, in certain contexts, the introduction of social media bans for children [1,11]. For example, the European Union has adopted a comprehensive set of legislative and policy initiatives designed to safeguard children in online environments while supporting their ability to engage meaningfully with the digital world. These efforts encompass regulations on digital services, audiovisual media, and personal data protection, as well as practical mechanisms such as helplines and reporting systems for harmful or illegal online content. Additionally, the European Union has developed a digital identity framework intended to provide secure and reliable age verification, thereby reducing children’s exposure to age-inappropriate material [12].
Moreover, national and regional governments have also implemented measures to enhance child protection in digital environments, with many countries introducing age thresholds and age-verification mechanisms [12]. While some jurisdictions have imposed bans on smartphone use in schools, others have focused on prevention through awareness-raising initiatives and educational programmes aimed at improving digital literacy among children and parents. In parallel, civil society organisations and international institutions contribute to promoting online child safety through research activities, public awareness campaigns, and the provision of dedicated support services. At the global level, a majority of countries have now implemented bans on mobile phone use in schools, with 58% having such policies in place. Notably, this expansion has occurred rapidly: in June 2023, fewer than one in four countries (24%) reported school smartphone bans, a proportion that increased to 40% by early 2025 and reached 58% by March 2026 [13]. According to a recent meta-analysis, school smartphone bans appear to have a statistically significant but relatively modest impact overall, with effects being more evident in the domain of social well-being than in academic performance. In particular, school smartphone bans have been associated with reductions in social problems, including bullying [14]. However, evidence regarding the impact of smartphone bans in schools is limited, and, therefore, further studies are needed to better understand the effects of the measure.
Against this backdrop, policy discussions in several countries have moved beyond school-based restrictions toward broader proposals advocating for universal age-based limits on social media access. Notably, countries such as Australia have initiated high-level debates and legislative initiatives aimed at restricting or prohibiting social media use for children under the age of 15 or 16, citing growing concerns about mental health, online safety, and the capacity of existing self-regulatory and parental control mechanisms to adequately protect children [15]. Australia has become the first country to introduce a nationwide restriction on social media access for children under 16 years of age. In Australia, the Online Safety Amendment (Social Media Minimum Age) Bill 2024 was enacted on 28 November 2024, establishing a mandatory minimum age of 16 years for holding accounts on designated social media platforms. The legislation came into force on 10 December 2025 and does not permit parental consent as an exception to the age requirement. Consequently, from that date onward, individuals under the age of 16 are prohibited from creating or retaining accounts on major social media services, including platforms such as Facebook, TikTok, Instagram, YouTube, Snapchat, and X, among others [15]. While the measure has been framed as a precautionary response to perceived shortcomings of platform self-regulation and parental mediation, it has also generated substantial public and academic debate, particularly regarding feasibility, circumvention, and potential implications for children’s rights and social participation [16,17,18,19,20,21,22].
As Australia’s policy is subject to ongoing evaluation, it has emerged as an influential reference point in international discussions on age-based social media restrictions. Furthermore, a growing number of countries have adopted or are actively considering age-based restrictions or outright bans on children’s access to social media platforms, reflecting increasing global concern about online harms. For instance, France has passed legislation to prohibit social media use for children under 15, with implementation expected to rely on age-verification mechanisms consistent with European Union digital regulation frameworks [23]. Moreover, Denmark has reached political agreement on a similar ban for under-15s, supported by national digital identity infrastructure to facilitate age verification [24]. Similarly, Austria and Spain are advancing policies to restrict access for children under 14 and under 16, respectively, while the United Kingdom has launched pilot programmes and consultations exploring under-16 bans and platform-level restrictions [25].
Within this rapidly evolving context, Greece’s social media ban for children under 15 from January 1, 2027 [26] aligns with a broader global policy trend, underscoring the importance of examining public opinion and its determinants to assess the societal legitimacy and feasibility of such measures. In this context, the aim of this study was to explore public opinion in Greece regarding a ban on social media use for children and to identify the factors influencing individuals’ opinion toward this policy measure. In particular, we examined sociodemographic factors, social media use, and political engagement as potential predictors of participants’ opinion toward a social media ban for children. To the best of our knowledge, this is the first study on this research field. By exploring public opinion on social media ban for children, our study seeks to inform public health strategies and policy decisions aimed at protecting children in the digital environment.
Methods
Study Design
On April 8, 2026, the Greek government announced that, as of January 1, 2027, access to social media will be prohibited for children under the age of 15 [26]. Specifically, the restriction will apply to platforms that allow users to create profiles, interact with one another, and share content accessible to others (e.g., posts, stories, videos, comments, likes, and followers). Platforms such as Facebook, Instagram, TikTok and Snapchat fall within this category. Consequently, from January 1, 2027, social media operating in Greece will be required to implement age-verification procedures for all user accounts, using technologies that may include official identification documents (e.g., national identity cards or passports) or biometric data (e.g., facial recognition or age estimation). It should be noted that the precise mechanisms for age verification have not yet been fully determined by the Greek government, as efforts are underway to establish a uniform age restriction across the European Union, with responsibility for compliance resting with the platforms. Nevertheless, as of January 1, 2027, access to social media by individuals under 15 years of age will be prohibited in Greece.
In this context, we conducted a cross-sectional study in Greece, two weeks after the announcement of the Greek government regarding the social media ban in children under the age of 15. Data were collected through an online survey administrated in April 2026. The questionnaire was developed using Google Forms and distributed through social media platforms (Facebook, Instagram, TikTok, and LinkedIn), resulting in a convenience sample. Eligibility criteria included: (a) age ≥18 years, (b) provision of informed consent, (c) proficiency in the Greek language, as the study was conducted in Greece, (d) access to the internet, and (e) possession of an active account on at least one social media platform. Exclusion criteria comprised individuals less than 18 years of age, illiterate populations, and non-Greek speakers. Greek-language versions of the study instruments were used; therefore, non-Greek speakers were excluded to minimize the risk of information bias. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [27].
Measurements
Sociodemographic Characteristics
Supplementary Table S1 presents the variables that we measured in our study and response options for each variable.
We measured the following sociodemographic characteristics as potential predictors of participants’ opinion toward a social media ban for children: (1) sex (males or females), (2) age (continuous variable), (3) children aged under 18 years (no or yes), (4) educational level (some high school or less, high school, College degree), and (5) financial status (self-rated scale from 0 [very poor] to 10 [very good]).
Social Media Use
Social media use was measured with the following variables: (1) number of accounts on social media (Facebook, Instagram, TikTok, YouTube, X [former Twitter], LinkedIn, Snapchat), (2) daily time spent on social media (continuous variable), and (3) frequency of posting on social media (never, about once a month, about once in two weeks, about once a week, two-four times a week, five-seven times a week).
Political Engagement
We measured political engagement with two questions. The first question asked, “How closely do you follow news about politics?” with answers in a five-point Likert scale; not closely at all (1), not very closely (2), somewhat closely (3), closely (4), very closely (5). Also, the second question asked, “How often do you talk to people about politics?” with answers in a five-point Likert scale; not at all (1), rarely (2), sometimes (3), often (4), very often (5). Then, responses to the two questions were summed to generate a total score ranging from 2 to 10. This composite score constituted the variable “political engagement”. Higher values on this variable indicated higher levels of political engagement.
Opinion on Social Media Ban for Children
Several questions were used to measure participants’ opinion on social media ban for children. We asked participants the following questions: (1) “In your opinion, at what age is it appropriate for a child to have a personal social media account?” with answers ranged from 10 to 17 years old, (2) “In your opinion, who should be responsible for setting limits on children’s use of social media?” with answers including parents, or governments, (3) “To what extent do you agree with the fact that the problematic social media use among children constitutes an important public health concern?” with answers in a five-point Likert scale; strongly disagree, disagree, neutral, agree, strongly agree, (4) “To what extent do you agree with the implementation of a social media ban for all children under age of 15?” with answers in a five-point Likert scale; strongly disagree, disagree, neutral, agree, strongly agree, (5) “Do you need more information from the government regarding the implementation of the ban?” with answers in a five-point Likert scale; strongly disagree, disagree, neutral, agree, strongly agree, (6) “Do you believe that a social media ban may lead parents to a false sense of security regarding their children’s safety, resulting in reduced engagement with their children’s digital education?” with answers in a five-point Likert scale; strongly disagree, disagree, neutral, agree, strongly agree, (7) “Do you believe that additional measures, beyond a social media ban, should be implemented to address the problem?” with answers in a five-point Likert scale; strongly disagree, disagree, neutral, agree, strongly agree, and (8) “Do you believe that a social media ban violates children’s rights?” with answers in a five-point Likert scale; strongly disagree, disagree, neutral, agree, strongly agree.
Furthermore, to assess participants’ confidence in the effectiveness of the social media ban, we used the following items: (1) Children will be able to find ways to create accounts on platforms to which the social media ban will be applied, (2) Children will create accounts on platforms that will be free and, therefore, less regulated, and (3) Social media platforms will fully comply with the legislation regarding the social media ban. Answers on theses three items were in a five-point Likert scale; strongly disagree (1), disagree (2), neutral (3), agree (4), strongly agree (5). Scores for the first two items were reverse-coded so that, across all three items, higher scores consistently indicated greater confidence in the effectiveness of the social media ban. Then, responses to the three items were summed to generate a total score ranging from 3 to 15. This composite score constituted the variable “confidence in the effectiveness of the social media ban”. Higher values on this variable indicated greater confidence in the effectiveness of the social media ban.
To assess participants’ views regarding the impact of the social media ban on children’s lives, the following items were used: (1) Social media ban will improve children’s mental health, (2) Social media ban will improve children’s sleep quality, and (3) Social media ban will improve children’s school performance. Answers on theses three items were in a five-point Likert scale; strongly disagree (1), disagree (2), neutral (3), agree (4), strongly agree (5). Responses to the three items were summed to generate a total score ranging from 3 to 15. This composite score constituted the variable “impact of the social media ban on children’s lives”. Higher values on this variable indicated more positive impact of the social media ban on children’s lives.
In addition, we examined whether participants perceived social media ban as more or less effective compared with alternative measures. Specifically, participants were asked, “Do you believe that a social media ban is a more effective measure compared with the following measures?” (1) Restricting the addictive features of social media platforms (e.g., infinite scrolling and constant notifications); (2) Prohibiting platforms from displaying inappropriate content (e.g., violent material, hate content, pornographic material, content related to criminal activity); (3) Integrating digital literacy courses into the school curriculum; (4) Parents setting reasonable limits on children’s social media use; and (5) Parents’ active involvement in children’s digital literacy. Two response options were provided for each alternative measure: (a) Yes, social medial ban is a more effective measure, (b) No, social media is a less effective measure. The social media ban was compared separately with each alternative measure in order to assess the relative importance participants attributed to each measure in comparison with the ban. By evaluating the social media ban separately against multiple alternative measures, the study provided a more nuanced understanding of public opinion regarding different policy and preventive strategies. Rather than assessing support for a social media ban in isolation, this approach allows for the examination of how participants prioritize the ban relative to other widely discussed public health and regulatory measures, such as platform design modifications, content regulation, digital literacy education, and parental involvement.
Finally, parents of children aged 8-17 years were asked whether they are familiar with and able to use available tools and applications that enable parental control over children’s access to social media. Additionally, we asked parents whether they are familiar with and able to use the application “Kids Wallet”. Responses in both questions were in a five-point Likert scale; strongly disagree (1), disagree (2), neutral (3), agree (4), strongly agree (5). The “Kids Wallet” is a Greek government mobile application designed for parents to manage their children's digital safety, monitor screen time, and verify age for services. It serves as a digital identity tool for children, offering parental control over online content. In particular, the “Kids Wallet” allows parents to configure which applications and websites their children can access, as well as the amount of time they spend on them. Also, the application provides a secure and reliable method for verifying children’s age online, protecting them from inappropriate content and potential dangers. Kids Wallet functions as a digital wallet in which the child can store their digital identity, facilitating access to services and applications that require age verification. Responses to the two items were summed to generate a total score ranging from 2 to 10. This composite score constituted the variable “parental familiarity with digital parental control tools”. Higher values on this variable indicated higher level of parents’ familiarity with digital parental control tools.
Ethical Issues
Ethical approval for the study protocol was obtained from the Ethics Committee of the Faculty of Nursing, National and Kapodistrian University of Athens (Approval No. 71; April 21, 2026). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki [28]. Prior to enrollment, participants received detailed information regarding the study’s objectives and procedures, and informed consent was obtained from all participants. Data were collected anonymously and participation was voluntary.
Statistical Analysis
Categorical variables are presented as frequencies (n) and percentages (%), whereas continuous variables are summarized using the mean, standard deviation (SD), median, minimum, and maximum values. The distribution of continuous variables was assessed using the Kolmogorov–Smirnov test and Q-Q plots, indicating normality. Sociodemographic variables, social media use, and political engagement were the independent variables. Scores on variables “confidence in the effectiveness of the social media ban”, “impact of the social media ban on children’s lives”, and “parental familiarity with digital parental control tools” were the dependent variables. Multivariable linear regression analyses were conducted to examine the independent associations of sociodemographic characteristics, social media use, and political engagement with the outcome variables. Adjusted beta coefficients, 95% confidence intervals (CIs), p-values, and adjusted R2 are reported. Multicollinearity was evaluated using variance inflation factors (VIF), with values above 4 suggesting potential collinearity. The assumptions of normality, homoscedasticity, and linearity were assessed by inspecting residual histograms and scatterplots of residuals versus predicted values. We performed chi-square test and chi-square trend test to examine associations observed between respondents’ general opinions on social media ban and sex and parental status (parents versus non-parents of children under 18 years of age). Moreover, we used Spearman correlation coefficient and independent samples t-test to examine associations between respondents’ general opinions on social media ban and age. Sample size was calculated using G*Power v.3.1.9.2. We included nine predictors in our multivariable models. Thus, considering an anticipated effect size of 0.03 between each independent variable and our outcomes, a statistical power of 99%, and a Type I error of 5%, the sample size was estimated to be 615 participants. Statistical significance was set at p < 0.05. All analyses were performed using IBM SPSS Statistics for Windows, version 28.0 (IBM Corp., Armonk, NY, USA).
Results
Sociodemographic Characteristics
Study sample included 619 participants. In our sample, 51.9% were females, while 48.1% were males. Among our participants, 32.1% had children aged 8-17 years. Mean age was 42.4 years (SD; 13.7) with a median value of 45, a minimum age of 18, and a maximum age of 72. Mean financial status score was 6.4 (SD; 1.6), with a median score of 7, a minimum score of 0, and a maximum score of 10. Table 1 shows the sociodemographic characteristics of the participants.
Social Media Use
Most participants had an account on Facebook (64.6%), Instagram (62.4%), YouTube (62.4%), and TikTok (32.8%). Most of them had accounts on two platforms (34.9%), while 24.7% had accounts on three platforms, 17.9% had accounts on one platform, and 17.1% had accounts on one platform. Mean daily time spent on social media was 2.7 hours (SD; 1.6), with a median time of 2 hours, a minimum value of thirty minutes, and a maximum value of 10 hours. In our sample, 40.7% posted once a month on social media, 33.8% did not post ever, 16.3% posted once in two weeks, 6.1% posted once a week, and 3.1% posted two-seven days a week. Table 2 shows the social media use of the participants.
Political Engagement
Among our participants, 85.9% reported that they follow somewhat/very closely news about politics, while 14.1% reported that they follow not closely at all/not very closely news about politics. Moreover, 47.7% reported that they talk sometimes to people about politics, 26.1% talk often/very often, and 26.2% talk rarely or not at all. The mean composite score for the variable “political engagement” was 6.4 (SD; 1.7), with a median score of 6, a minimum score of two, and a maximum score of 10. Table 3 shows the political engagement of the participants.
Opinion on Social Media Ban for Children
Opinion in General
Table 4 shows participants’ opinion on social media ban for children. In the sample study, 37.6% of respondents reported that the appropriate age for a child to have a personal social media account is 17 years, followed by 34.1% who reported 16 years, 17.0% who reported 15 years, 9.0% who reported 14 years, 1.8% who reported 13 years, and 0.5% who reported 12 years. Most participants (76.6%) stated that parents should be responsible for setting limits on children’s use of social media, whereas 23.4% reported that this responsibility should rest with governments. Most of the participants stated that the problematic social media use among children constitutes an important public health concern (94.5%). In our sample, 69.2% agreed with the implementation of a social media ban for all children under age of 15. Also, most of our sample (92.7%) reported that they need more information from the government regarding the implementation of the ban. Additionally, 86.5% believed that additional measures, beyond a social media ban, should be implemented to address the problem.
Supplementary Table S2 shows the association between participants’ general opinion on social media ban for children and sex. Female respondents were more likely than males to report that the appropriate age for a child to hold a personal social media account is between 16 and 17 years (81.9% vs. 60.7%, p < 0.001). In addition, females more frequently endorsed the view that parents should bear primary responsibility for setting limits on children’s social media use (80.4% vs. 72.5%, p = 0.021). A higher proportion of females also perceived problematic social media use among children as a significant public health concern (97.2% vs. 91.6%, p = 0.001). Furthermore, females were more likely to express concern that the implementation of a social media ban could lead parents to a false sense of security regarding their children’s online safety, potentially resulting in reduced parental engagement in children’s digital education (35.5% vs. 20.8%, p = 0.023).
Supplementary Table S3 shows the association between participants’ general opinion on social media ban for children and age. Older participants were more likely to believe that children should be allowed to hold a personal social media account at a later age (p < 0.001). They also more frequently agreed that problematic social media use among children constitutes a significant public health concern (p < 0.001). In contrast, younger participants expressed stronger support for the implementation of a universal social media ban for children under the age of 15 (p = 0.019) and reported a greater need for additional information from governmental authorities regarding the implementation of such a ban (p < 0.001). Furthermore, younger respondents were more likely to consider that supplementary measures, beyond a social media ban, should be introduced to effectively address the issue (p < 0.001).
Supplementary Table S4 shows the association between participants’ general opinion on social media ban for children and parental status (parents versus non-parents of children under 18 years of age). Parents were more likely to consider that additional measures, beyond a social media ban, should be implemented to address the issue (92.5% vs. 83.6%, p=0.001).
Confidence in the Effectiveness of the Social Media Ban
Most participants agreed that children will be able to find ways to create accounts on platforms to which the social media ban will be applied (88.6%), and they will create accounts on platforms that will be free and, therefore, less regulated (86.7%). Also, only 32.4% believed that social media platforms will fully comply with the legislation regarding the social media ban. The mean composite score for the variable “confidence in the effectiveness of the social media ban” was 6.7 (SD; 2.0), with a median score of 7, a minimum score of three, and a maximum score of 14.
Impact of the Social Media Ban on Children’s Lives
About half of the participants considered that social media ban will improve children’s mental health (45.6%) and school performance (47.5%). A higher percentage (68%) reported that social media ban will improve children’s sleep quality. The mean composite score for the variable “impact of the social media ban on children’s lives” was 10.5 (SD; 2.5), with a median score of 11, a minimum score of three, and a maximum score of 15.
Parental Familiarity with Digital Parental Control Tools
Among parents of children aged 8-17 years, 20.6% stated that they are not familiar with and able to use available tools and applications that enable parental control over children’s access to social media. Additionally, 63.8% reported that they are not familiar with and able to use the application “Kids Wallet”. The mean composite score for the variable “parental familiarity with digital parental control tools” was 5.4 (SD; 2.2), with a median score of 5, a minimum score of two, and a maximum score of 10.
Effectiveness of Social Media Ban Compared to Alternative Measures
In the sample, most participants favored targeted interventions over a social media ban, including digital literacy courses in schools (85.8%), active parental involvement in digital literacy (77.5%), prohibition of inappropriate content (76.1%), reasonable parental limits on social media use (72.1%), and restriction of addictive platform features (69.3%).
Dependent Variable: Confidence in the Effectiveness of the Social Media Ban
Table 5 reports the results of the multivariable linear regression model with score on the variable “confidence in the effectiveness of the social media ban” as the dependent variable. We found that increased age is associated with increased confidence in the effectiveness of the social media ban (adjusted coefficient beta = 0.013, 95% CI = 0.001 to 0.026, p=0.040). In other words, older participants showed higher levels of confidence in the effectiveness of the social media ban. The VIF values obtained from the final model indicated that multicollinearity was not a concern since values ranged from 1.101 to 1.405. Supplementary Figure S1 demonstrates that the assumption of multivariate normality is met, as the residuals align with a normal distribution. Supplementary Figure S2 supports the assumptions of homoscedasticity and linearity for the multivariable model in which score on the variable “confidence in the effectiveness of the social media ban” is the dependent variable.
Dependent Variable: Impact of the Social Media Ban on Children’s Lives
Table 6 reports the results of the multivariable linear regression model with score on the variable “impact of the social media ban on children’s lives” as the dependent variable. We found that females (adjusted coefficient beta = 1.528, 95% CI = 1.154 to 1.902, p<0.001) and participants with a college degree (adjusted coefficient beta = 0.446, 95% CI = 0.039 to 0.853, p=0.032) have higher score on the variable “impact of the social media ban on children’s lives”. Moreover, our results showed a positive association between age (adjusted coefficient beta = 0.036, 95% CI = 0.021 to 0.050, p<0.001), financial status (adjusted coefficient beta = 0.154, 95% CI = 0.041 to 0.268, p=0.008), total number of accounts on social media (adjusted coefficient beta = 0.189, 95% CI = 0.028 to 0.351, p=0.021), daily time spent on social media (adjusted coefficient beta = 0.187, 95% CI = 0.059 to 0.316, p=0.004), and impact of the social media ban on children’s lives. The VIF values obtained from the final model indicated that multicollinearity was not a concern since values ranged from 1.101 to 1.405. Supplementary Figure S3 demonstrates that the assumption of multivariate normality is met, as the residuals align with a normal distribution. Supplementary Figure S4 supports the assumptions of homoscedasticity and linearity for the multivariable model in which score on the variable “impact of the social media ban on children’s lives” is the dependent variable.
Dependent Variable: Parental Familiarity with Digital Parental Control Tools
Table 7 reports the results of the multivariable linear regression model with score on the variable “parental familiarity with digital parental control tools” as the dependent variable. This analysis included responses exclusively from parents with children under the age of 18 years (n = 199), as it would be unreasonable to expect participants without children to use digital parental control tools. We found that reduced age is associated with increased parental familiarity with digital parental control tools (adjusted coefficient beta = -0.060, 95% CI = -0.108 to -0.011, p=0.017). Additionally, multivariable analysis identified a positive association between total number of accounts on social media (adjusted coefficient beta = 0.571, 95% CI = 0.343 to 0.799, p<0.001) and parental familiarity with digital parental control tools. The VIF values obtained from the final model indicated that multicollinearity was not a concern since values ranged from 1.059 to 1.730. Supplementary Figure S5 demonstrates that the assumption of multivariate normality is met, as the residuals align with a normal distribution. Supplementary Figure S6 supports the assumptions of homoscedasticity and linearity for the multivariable model in which score on the variable “parental familiarity with digital parental control tools” is the dependent variable.
Discussion
Following Australia’s introduction of a nationwide ban on social media access for individuals under 16 years of age in December 2025, several countries have adopted legislative measures requiring age verification for social media platforms in an effort to mitigate online harms. For instance, European countries are actively considering similar restrictions, as the European Commission has signaled its intention to facilitate a coordinated, bloc-wide approach through the recent finalization of a standardized age-verification application [29]. In this context, understanding public opinion toward social media ban for children is essential, as societal attitudes play a critical role in determining the acceptability, legitimacy, and effectiveness of regulatory interventions such as social media ban. Greece constitutes a particularly relevant context for such investigation since on 8 April 2026, the Greek government announced a forthcoming regulatory measure under which children under 15 years of age will be restricted from accessing social media platforms, with implementation scheduled for 1 January 2027. Considering that the literature on this research field is scarce, we performed a study to explore public opinion regarding a ban on social media use for children and to identify the factors influencing individuals’ opinion toward this policy measure.
A key finding of this study is that an overwhelming majority of participants (94.5%) perceived problematic social media use among children as an important public health concern. This high level of consensus suggests that concerns related to children’s engagement with social media are not limited to individual or family contexts but are increasingly understood as a collective issue warranting population-level attention. Public recognition of problematic social media use as a public health problem is a critical precondition for the acceptance and effectiveness of regulatory and preventive interventions. In this context, our findings align with a growing body of literature highlighting the potential negative consequences of excessive or problematic social media use on children’s mental health, wellbeing, and development [2,3,4,6,7,8,10]. The widespread perception of risk identified in our sample may partly explain the increasing societal openness toward age-based restrictions and regulatory measures observed internationally. The near-universal acknowledgment of the issue in this study therefore underscores the relevance of examining not only the effectiveness but also the social acceptability of policies such as social media bans for children.
In addition, nearly seven in ten participants (69.2%) expressed agreement with the implementation of a social media ban for all children, indicating substantial public support for this restrictive policy response. To the best of our knowledge, there are no studies that examine the public opinion toward social media ban for children. However, there are three polls including adults that confirm our findings [30,31,32]. In particular, 70% of adults in six European countries (i.e., United Kingdom, France, Germany, Italy, Spain, and Poland) support social media ban for children under the age of 16 [30]. There is a significant variation between countries since support for a ban at 16 is very low (53%) in Poland compared to Spain (68%), Italy (70%), Germany (74%), United Kingdom (76%), and France (79%). A similar poll including adults only in United Kingdom found that 78% support social media ban for children under the age of 16 [31]. Findings from another poll in US and Australia found that 58% of US parents of children aged 10-17, and 65% of Australian parents are in support [32]. In our sample, a slightly higher percentage of parents (71.4%) supported the ban. We should notice that 30.8% of our participants and similar percentages (22% to 35%) in the other three polls [30,31,32] disagree or they are opposite to the ban. This absence of unanimous agreement highlights ongoing societal ambivalence regarding social media ban for children. This divergence likely reflects concerns related to effectiveness, feasibility, enforcement, children’s autonomy, and the potential displacement of responsibility from parents to the state. These concerns are further substantiated by our findings and by the three opinion polls discussed above. Specifically, 44.7% of adults across the United Kingdom, France, Germany, Italy, Spain, and Poland reported that a social media ban would be either not very effective or not effective at all, whereas only 37% expressed optimism regarding its potential effectiveness [30]. Comparable apprehensions were identified in a separate poll conducted in the United Kingdom, where 75% of adults believed that children would lie about their age, use falsified identification, or exploit technological workarounds to create accounts on banned platforms. In addition, 86% of respondents indicated that regulators lack sufficient power to hold technology companies accountable, and 67% warned that such bans could drive children toward less regulated and potentially more hazardous online environments, including the dark web [31]. Similarly, limited confidence in the effective implementation of social media bans was observed among parents in other countries. Only 35% of parents in the United States and 29% in Australia reported confidence in their government’s ability to successfully enforce a national social media ban. Furthermore, 53% of U.S. parents and 54% of Australian parents believed that children would nonetheless find ways to access prohibited platforms [32]. Our results are consistent with these international findings. The vast majority of participants in our study (88.6%) indicated that children would be able to circumvent restrictions and create accounts on platforms subject to a ban. Additionally, 86.7% believed that children would migrate to platforms that remain accessible and are therefore likely to be subject to less regulatory oversight. Notably, only 31.4% of respondents expressed confidence that technology companies would fully comply with the legislative requirements associated with the implementation of a social media ban. Moreover, early data from Australia confirm the concerns expressed by our participants. An online poll conducted between 12 and 31 March 2026 surveyed 1,050 children aged 12-15 years and revealed that 61% of participants who had previously maintained accounts on restricted social media platforms continued to have access to one or more active accounts following the implementation of the ban. In addition, 70% of children who reported ongoing use of restricted platforms indicated that circumventing the ban was “easy”. Notably, 21% of children reported having created accounts on platforms they had not previously used. In terms of perceived online safety, 51% of children stated that the ban had not altered how safe they felt online, while 14% reported feeling less safe online after the social media ban came into effect [33].
Taken together, these findings suggest that while a social media ban for children may enjoy broad social legitimacy, its long-term acceptance and effectiveness may depend on clear communication, robust implementation strategies, and the parallel development of supportive educational and preventive initiatives. Our study supports this approach, since most of participants (86.5%) believed that additional measures should be implemented to effectively address the problematic social media use among children. These measures should include digital literacy courses in schools (85.8%), active parental involvement in digital literacy (77.5%), prohibition of inappropriate social media content (76.1%), reasonable parental limits on social media use (72.1%), and restriction of addictive platform features (69.3%). Thus, beyond support for a social media ban, participants in this study expressed strong agreement that additional measures are necessary to effectively address problematic social media use among children. These proposed measures suggest a nuanced understanding of the issue as a complex public health challenge. Rather than viewing prohibition as a standalone solution, respondents appear to favor a comprehensive, multi-level approach that combines regulation, education, and shared responsibility across multiple stakeholders. This perspective aligns with public health frameworks that emphasize prevention, empowerment, and environmental modification alongside regulatory measures [20,34,35,36]. The strong endorsement of educational and parental strategies also indicates recognition that children’s digital resilience and critical skills are essential components of long-term harm reduction [21,36,37]. Collectively, these findings suggest that public support for restrictive policies such as social media bans is conditional upon their integration into a broader strategy that addresses both individual behaviors and the structural features of digital platforms [36,38,39,40,41].
Furthermore, an overwhelming majority of our participants (92.7%) reported a need for more detailed information from governmental authorities regarding the implementation of a social media ban. Australian parents confirm our results since 47% of them stated that they do not understand how the Australian government’s social media ban is working [32]. Special attention should be given to parents’ education regarding digital parental control groups since only 38.2% of parents in our sample believed that they are familiar with digital parental control groups. Moreover, only 22.2% are familiar with the application “Kids Wallet”, i.e., a Greek government mobile application designed for parents to manage their children's digital safety. These findings highlight a critical gap between general support for regulatory measures and understanding of their practical application. High demand for official guidance suggests that public acceptance of a social media ban is closely linked to clarity about its scope, enforcement mechanisms, age-verification processes, and the roles assigned to parents, schools, and platform providers. Insufficient communication may risk uncertainty, mistrust, or unrealistic expectations regarding the protective capacity of such policies. From a public health perspective, transparent and proactive dissemination of information is essential not only to ensure compliance but also to prevent a false sense of security that could reduce parental engagement with children’s digital education [38,40,41].
This study suggests a positive association between age and confidence in the effectiveness of the social media ban. Additionally, we found a positive association between age and positive impact of ban on children’s lives. These findings provide important insight into generational differences in attitudes toward regulatory interventions. Older participants may be more inclined to view legislative measures as effective tools for risk mitigation, reflecting broader trust in institutional regulation or greater familiarity with policy-driven public health interventions. Older individuals often show higher levels of trust in public institutions and authorities compared to younger, more skeptical generations. This trust often translates into a belief in the necessity and effectiveness of governmental intervention. In contrast to younger respondents, older individuals may also be less embedded in digital environments and therefore perceive restrictions as more feasible and enforceable [42,43,44]. Additionally, increased age is often associated with heightened awareness of long-term societal and health consequences, which may translate into stronger beliefs in the preventive potential of regulatory approaches [45]. Age-related differences in risk perception may also play a role, as older individuals are more likely to prioritize long-term societal and developmental consequences, whereas younger cohorts may focus more strongly on immediate personal experience [46]. This finding suggests that confidence in the effectiveness of a social media ban is shaped not only by perceptions of children’s digital risks but also by broader life experience, normative beliefs about governance, and attitudes toward state intervention. Understanding these age-related differences is essential for anticipating public responses to policy implementation and for tailoring communication strategies to address skepticism among younger populations, who may have greater direct exposure to social media and more reservations regarding the practicality of restrictive measures.
Another finding of this study is that participants with a higher socioeconomic level, as illustrated by educational level and financial status, were more likely to perceive a social media ban as having a positive impact on children’s lives. Higher socioeconomic level has been associated with greater exposure to scientific evidence, higher health literacy, and increased capacity to evaluate complex risk–benefit trade-offs, particularly in public health contexts [47,48,49]. Individuals with higher socioeconomic may therefore be more aware of research documenting the potential adverse effects of excessive or problematic social media use among children and more receptive to preventive, population-level policy measures. Moreover, education has been shown to correlate with stronger support for evidence-based policymaking and greater trust in regulatory institutions, especially when interventions target collective goods such as child protection and wellbeing [48]. In contrast, lower educational attainment has been linked to increased skepticism toward regulatory policies, often driven by concerns about feasibility, unintended consequences, or perceptions of overreach into private or family domains [50].
An interesting finding of this study is that participants with higher levels of social media use, such as greater daily time spent on social platforms, were more likely to believe that a social media ban would have a positive impact on children’s lives. This pattern may reflect the role of experiential familiarity in shaping risk awareness and policy attitudes. Individuals who engage intensively with social media may be more directly exposed to its negative dimensions, including compulsive use tendencies, time displacement, emotional exhaustion, and exposure to harmful or distressing content [51,52]. Such firsthand experience may foster a more critical and reflective evaluation of social media environments, particularly with regard to their suitability for children, whose self-regulatory capacities are still developing. Frequent users often demonstrate heightened recognition of addictive design features, such as infinite scroll, algorithmic personalization, and constant notifications, which are specifically engineered to maximize engagement [53]. Awareness of these mechanisms may increase support for external regulatory measures, including bans, as protective tools for children who may be especially vulnerable to persuasive technology. In contrast, participants with lower levels of social media use may have more limited exposure to these challenges and therefore perceive bans as less necessary or less beneficial.
A similar finding in our study is that parents with higher levels of social media use, as it is indicated by a greater total number of social media accounts, reported higher familiarity with digital parental control tools. Parents’ own digital skills and engagement with online environments are key determinants of their ability to implement effective strategies, including the use of technical controls and monitoring tools [54,55]. Greater personal involvement with social media platforms may enhance technical competence and awareness of platform-specific safety features, thereby increasing confidence in navigating and applying parental control technologies. Parents who are more digitally literate are more likely to adopt digital parental control tools rather than relying solely on restrictive or avoidance-based strategies. Moreover, familiarity with social media ecosystems may heighten awareness of potential online risks, motivating proactive efforts to safeguard children through the use of available digital tools [56,57]. In contrast, parents with lower levels of social media use may face structural and knowledge-based barriers that limit their understanding or uptake of parental control technologies, even when such tools are readily available.
Limitations
Several limitations should be considered when interpreting the findings of this study. First, the cross-sectional design restricts the ability to infer causal relationships between participant characteristics and attitudes toward a social media ban. The observed associations reflect associations at a single point in time and do not allow conclusions regarding directionality or changes in public opinion over time. Second, the study relied on self-reported data, which may be subject to reporting biases, including social desirability and recall bias. Participants may have overstated socially acceptable attitudes, such as concern for children’s wellbeing or support for protective policies, or may not have accurately reported their own social media use patterns or familiarity with digital tools. Third, despite efforts to capture a broad range of sociodemographic characteristics, the sample may not be fully representative of the general population. Potential selection bias may have arisen if individuals with stronger opinions about children’s digital wellbeing or greater interest in social media regulation were more likely to participate. Consequently, the generalizability of the findings to other populations or cultural contexts should be undertaken with caution. Fourth, attitudes toward a social media ban were assessed in a hypothetical or prospective policy context. Public opinion may change following actual implementation, particularly as individuals gain practical experience with enforcement mechanisms, age verification processes, and unintended consequences. As a result, expressed support or skepticism may not directly translate into real-world acceptance or compliance. There is a need for further studies, after the implementation of the social media ban, in order to achieve more valid findings. Fifth, although multiple influencing factors were examined, it is possible that relevant variables were not fully captured. Factors such as political orientation, trust in government, prior exposure to digital literacy training, or personal experiences with online harm were not assessed and may further shape attitudes toward regulatory interventions. Finally, the rapidly evolving nature of digital technologies and social media platforms represents an inherent limitation. Public perceptions and policy preferences may shift as platforms introduce new features, regulatory environments change, or additional evidence emerges regarding the impacts of social media on children’s wellbeing. Despite these limitations, the study provides valuable insight into public perspectives on social media bans for children and highlights key considerations for policymakers. In brief, future research employing longitudinal designs, representative sampling, and qualitative approaches could further enhance understanding of how public attitudes develop and how they interact with policy implementation over time.
Conclusions
This study examined public opinion toward a social media ban for children and identified key factors influencing individuals’ opinion for this policy measure. The findings demonstrate a high level of societal concern regarding children’s social media use, with the vast majority of participants perceiving problematic use as a significant public health issue. This widespread recognition positions children’s digital wellbeing firmly within the remit of public health and justifies proactive governmental intervention.
Importantly, while a substantial proportion of respondents supported the implementation of a social media ban for children, public endorsement was not unconditional. Participants consistently emphasized that restrictive measures should not operate in isolation but must be embedded within a broader, integrated policy framework. The strong support for complementary measures signals public preference for a comprehensive and preventive approach rather than a purely prohibitive one.
From a policy perspective, our findings have several implications. First, social media bans targeting children are likely to achieve greater social legitimacy and compliance when framed as part of a multi-layered strategy that balances protection with empowerment. Policymakers should therefore avoid presenting bans as standalone solutions and instead articulate how regulatory measures will coexist with educational, familial, and platform-level responsibilities. Second, the observed sociodemographic and social media use differences in individuals’ opinion underscore the need for tailored policy communication. Clear, accessible, and audience-specific messaging may be critical to addressing skepticism, managing expectations, and ensuring inclusive public engagement across population groups.
A particularly salient finding is the near-universal demand for clearer governmental guidance on how a social media ban would be implemented. This highlights that policy effectiveness depends not only on legislative design but also on transparent communication, public consultation, and ongoing information provision. Without explicit guidance on enforcement mechanisms, age verification processes, and the respective roles of parents, schools, and platforms, even well-supported policies risk misunderstanding, uneven implementation, or a false sense of security that could undermine parental engagement.
Overall, the results suggest that successful regulation of children’s social media use requires a shift from binary debates about bans toward holistic, evidence-informed policy frameworks that combine regulation, education, and shared accountability. Policymakers are encouraged to leverage public support for child protection while simultaneously investing in digital literacy initiatives, parental empowerment, and platform regulation to ensure sustainable and equitable outcomes.
Finally, considering the limitations of our study and the fact that literature on this research field is scarce there is an urgent need for further well established studies. Importantly, future research should evaluate how public attitudes evolve following ban implementation and assess the effectiveness of integrated policy approaches in improving children’s digital wellbeing over time.
Supplementary Materials
The following supporting information can be downloaded at the website of this paper posted on Preprints.org.
Funding
none.
Conflicts of Interest
none.
References
- OECD How’s Life for Children in the Digital Age? OECD Publishing, 2025; ISBN 978-92-64-44251-1.
- OECD PISA 2022 Results (Volume I): The State of Learning and Equity in Education; PISA; OECD Publishing, 2023; ISBN 978-92-64-65419-8.
- Agyapong-Opoku, N.; Agyapong-Opoku, F.; Greenshaw, A.J. Effects of Social Media Use on Youth and Adolescent Mental Health: A Scoping Review of Reviews. Behav. Sci. 2025, 15, 574. [Google Scholar] [CrossRef]
- Bottaro, R.; Faraci, P. The Use of Social Networking Sites and Its Impact on Adolescents’ Emotional Well-Being: A Scoping Review. Curr. Addict Rep. 2022, 9, 518–539. [Google Scholar] [CrossRef]
- Verduyn, P.; Gugushvili, N.; Massar, K.; Täht, K.; Kross, E. Social Comparison on Social Networking Sites. Curr. Opin. Psychol. 2020, 36, 32–37. [Google Scholar] [CrossRef]
- OECD Students, Digital Devices and Success. In OECD Education Policy Perspectives; 2024; Vol. 102.
- Piteo, E.M.; Ward, K. Social Networking Sites and Associations with Depressive and Anxiety Symptoms in Children and Adolescents - a Systematic Review. Child. Adolesc. Ment. Health 2020, 25, 201–216. [Google Scholar] [CrossRef] [PubMed]
- Twenge, J.M. Increases in Depression, Self-Harm, and Suicide Among U.S. Adolescents After 2012 and Links to Technology Use: Possible Mechanisms. Psychiatr. Res. Clin. Pract. 2020, 2, 19–25. [Google Scholar] [CrossRef] [PubMed]
- Sun, Y.; Zhang, Y. A Review of Theories and Models Applied in Studies of Social Media Addiction and Implications for Future Research. Addctv. Behav. 2021, 114, 106699. [Google Scholar] [CrossRef]
- Wadsley, M.; Ihssen, N. A Systematic Review of Structural and Functional MRI Studies Investigating Social Networking Site Use. Brain Sci. 2023, 13, 787. [Google Scholar] [CrossRef]
- Laaninen, T.; Bol, J. Youth and Social Media; European Parliament: Strasbourg, 2025. [Google Scholar]
- Niestadt, M. Protecting Children Online; European Parliament: Strasbourg, 2025. [Google Scholar]
- UNESCO Phone Bans in Schools Are Spreading Worldwide as the Policy Debate Rages On. Available online: https://www.unesco.org/gem-report/en/articles/phone-bans-schools-are-spreading-worldwide-policy-debate-rages.
- Böttger, T.; Zierer, K. To Ban or Not to Ban? A Rapid Review on the Impact of Smartphone Bans in Schools on Social Well-Being and Academic Performance. Educ. Sci. 2024, 14, 906. [Google Scholar] [CrossRef]
- Australian Government Online Safety Amendment (Social Media Minimum Age) Act 2024. 2026.
- Champion, K.E.; Birrell, L.; Smout, S.; Teesson, M.; Slade, T. Social Media in Children and Young People – Time for a Ban? Beyond the Ban – Empowering Parents and Schools to Keep Adolescents Safe on Social Media. Child. Adoles Ment. Health 2025, 30, 411–413. [Google Scholar] [CrossRef]
- Christensen, H.; Mackinnon, A. Do Social-Media Bans Benefit Young People? These Data Could Offer Clues. Nature 2025, 645, 38–40. [Google Scholar] [CrossRef]
- Editorial Social Media Ban: A Band-Aid on Digital Wounds? Lancet Public Health 2026, 11, e137. [CrossRef]
- Editorial Beyond the Social Media Ban. Lancet Digit Health 2025, 7, e232. [CrossRef]
- Fardouly, J. Potential Effects of the Social Media Age Ban in Australia for Children Younger than 16 Years. Lancet Digit Health 2025, 7, e235–e236. [Google Scholar] [CrossRef] [PubMed]
- Campbell, M. Australia’s Social-Media Ban Won’t Work - There Are Better Ways to Keep Kids Safe. Nature 2025, 637, 519. [Google Scholar] [CrossRef]
- Fieldhouse, R.; Basu, M. Australia’s World-First Social Media Ban Is a “natural Experiment” for Scientists. Nature 2025. [Google Scholar] [CrossRef]
- Gkritsi, E.; Durand, K. France’s under-15 Social Media Ban: 5 Things to Know 2026.
- Malik, A. These Are the Countries Moving to Ban Social Media for Children 2026.
- Bhaimiiya, S. Countries around the World Are Considering Teen Social Media Bans – Why Experts Warn It’s a ‘Lazy’ Fix 2026.
- Rawnsley, J. Greece to Ban Social Media for Under-15s from next Year 2026.
- Von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gøtzsche, P.C.; Vandenbroucke, J.P. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies. J. Clin. Epidemiol. 2008, 61, 344–349. [Google Scholar] [CrossRef]
- World Medical Association World Medical Association. Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA 2013, 310, 2191. [Google Scholar] [CrossRef]
- European Commision Statement by President von Der Leyen with Executive Vice-President Virkkunen on the Digital Age Verification App. 2026.
- Yougov Most Europeans in Six Countries Support Banning Social Media for Under-16s. Available online: https://yougov.com/en-gb/articles/54429-most-europeans-in-six-countries-support-banning-social-media-for-under-16s.
- The Harris Poll UK 78% Support Banning Under-16s from Social Media – but Majority Don’t Think It Will Work. Available online: https://www.theharrispoll.co.uk/media/78-support-banning-under-16s-from-social-media-but-majority-dont-think-it-will-work.
- Family Online Safety Institute A Ban Briefing: Children and Parents’ Perceptions of Social Media and Classroom Smartphone Bans in the U.S. and Australia. Available online: https://fosi.org/wp-content/uploads/2025/12/Children-Parents-Perceptions-of-Social-Media-and-Classroom-Smartphone-Bans-in-the-U.S.-and-Australia.pdf.
- Molly Rose Foundation More than 60% of Australian Children Still Using Social Media despite Ban for Under-16s, Research Shows. Available online: https://mollyrosefoundation.org/more-than-60-of-australian-children-still-using-social-media-despite-ban-for-under-16s-research-shows/.
- Stevens, M.W.R.; Radunz, M.; Király, O.; Griffiths, M.D.; Delfabbro, P.; Demetrovics, Z.; Czakó, A.; King, D.L. Beyond the Debate: Toward Pragmatic Evaluation of Australia’s Social Media Age Restrictions. Lancet Reg. Health-West. Pac. 2026, 68, 101818. [Google Scholar] [CrossRef]
- Fatt, S.; Fardouly, J. Social Media in Children and Young People - Time for a Ban? Weighing up the Implications and Limitations of Age-Based Social Media Restrictions. Child. Adolesc. Ment. Health 2025, 30, 414–415. [Google Scholar] [CrossRef] [PubMed]
- Weeramanthri, T. A World-First Social Media Ban: Lessons from Australia in a Globalised Public Policy Environment. J. Public Health Policy 2026. [Google Scholar] [CrossRef]
- Skinner, A.; Foljambe, R. Social Media in Children and Young People - Time for a Ban? It Is Time to Take a Precautionary Approach. Why Health Professionals Are Calling for a Ban on Social Media for under-16s. Child. Adolesc. Ment. Health 2025, 30, 419–421. [Google Scholar] [CrossRef]
- Redlich, C.; Lazeri, L.; Maurer, J.; Hall, J.; Azzopardi-Muscat, N. Social Media in Children and Young People - Time for a Ban? Beyond Bans: Addressing the Digital Determinants of Youth Mental Health and Well-Being in the European Region. Child. Adolesc. Ment. Health 2025, 30, 408–410. [Google Scholar] [CrossRef]
- Welsey-Smith, O.; Fleming, T. Social Media in Children and Young People - Time for a Ban? From Polarised Debate to Precautionary Action - a Population Mental Health Perspective on Social Media and Youth Well-Being. Child. Adolesc. Ment. Health 2025, 30, 416–418. [Google Scholar] [CrossRef]
- Mirza, T. Balancing Risks and Resilience: Evaluating the Impact of Australia’s Social Media Ban on Adolescent Mental Health. Australas. Psychiatry 2025, 33, 768–769. [Google Scholar] [CrossRef] [PubMed]
- Wiederhold, B.K. Are Social Media Bans the Solution to the Youth Mental Health Crisis? Some Governments Think So. Cyberpsychology Behav. Soc. Netw. 2025, 28, 295–297. [Google Scholar] [CrossRef] [PubMed]
- OECD OECD Survey on Drivers of Trust in Public Institutions – 2024 Results: Building Trust in a Complex Policy Environment; OECD Publishing, 2024; ISBN 978-92-64-33830-2.
- Stockemer, D.; Kolodziejczyk, K.; Buckle, M. Age and Trust in Government: A Generational Analysis. In Democracy Under Scrutiny;Contributions to Political Science; Stockemer, D., Abou-El-Kheir, E., Kolodziejczyk, K., Eds.; Springer Nature Switzerland: Cham, 2025; pp. 93–107. ISBN 978-3-032-06595-7. [Google Scholar]
- Schroeder, K.A.; Creecy, C.A.; Perkins, E.L.; Carter, K.E. The Impact of Age and Gender on Trust in the U.S. Federal Government. PsiChiJournal 2023, 28, 107–113. [Google Scholar] [CrossRef]
- Gozgor, G. Global Evidence on the Determinants of Public Trust in Governments during the COVID-19. Appl. Res. Qual. Life 2022, 17, 559–578. [Google Scholar] [CrossRef]
- Slovic, P. The Perception of Risk, 1st ed.; Routledge, 2016; ISBN 978-1-315-66177-3. [Google Scholar]
- Cutler, D.M.; Lleras-Muney, A. Understanding Differences in Health Behaviors by Education. J. Health Econ. 2010, 29, 1–28. [Google Scholar] [CrossRef]
- OECD Government at a Glance 2025; Government at a Glance; OECD Publishing, 2025; ISBN 978-92-64-31390-3.
- Soetekouw, L.; Angelopoulos, S. Digital Resilience Through Training Protocols: Learning To Identify Fake News On Social Media. Inf. Syst. Front 2024, 26, 459–475. [Google Scholar] [CrossRef]
- Ardelia, V.; Jaroah, S. Exploring Social Media Use Integration Based on Educational Level Among Emerging Adults. Int. J. Curr. Educ. Res. 2023, 2, 106–114. [Google Scholar] [CrossRef]
- Andreassen, C.S.; Pallesen, S.; Griffiths, M.D. The Relationship between Addictive Use of Social Media, Narcissism, and Self-Esteem: Findings from a Large National Survey. Addctv. Behav. 2017, 64, 287–293. [Google Scholar] [CrossRef]
- Kuss, D.; Griffiths, M. Social Networking Sites and Addiction: Ten Lessons Learned. IJERPH 2017, 14, 311. [Google Scholar] [CrossRef]
- Montag, C.; Lachmann, B.; Herrlich, M.; Zweig, K. Addictive Features of Social Media/Messenger Platforms and Freemium Games against the Background of Psychological and Economic Theories. IJERPH 2019, 16, 2612. [Google Scholar] [CrossRef] [PubMed]
- Livingstone, S.; Helsper, E.J. Parental Mediation of Children’s Internet Use. J. Broadcast. Electron. Mediu. 2008, 52, 581–599. [Google Scholar] [CrossRef]
- Livingstone, S.; Mascheroni, G.; Dreier, M.; Chaudron, S.; Lagae, K. How Parents of Young Children Manage Digital Devices at Home: The Role of Income, Education and Parental Style; London School of Economics, 2015. [Google Scholar]
- Fidan, N.K.; Olur, B. Examining the Relationship between Parents’ Digital Parenting Self-Efficacy and Digital Parenting Attitudes. Educ. Inf. Technol. (Dordr) 2023, 1–16. [Google Scholar] [CrossRef]
- Banić, L.; Orehovački, T. A Comparison of Parenting Strategies in a Digital Environment: A Systematic Literature Review. Multimodal Technol. Interact. 2024, 8, 32. [Google Scholar] [CrossRef]
Table 1.
Sociodemographic characteristics of the participants.
| Characteristics | N | % |
|---|---|---|
| Sex | ||
| Males | 298 | 48.1 |
| Females | 321 | 51.9 |
| Age, mean, standard deviation | 42.4 | 13.7 |
| Children aged 8-17 years | ||
| No | 420 | 67.9 |
| Yes | 199 | 32.1 |
| Educational level | ||
| High school | 274 | 44.3 |
| College degree | 345 | 55.7 |
| Financial status, mean, standard deviation | 6.4 | 1.6 |
Table 2.
Social media use of the participants.
| Social media use | N | % |
|---|---|---|
| Accounts on social media | ||
| 400 | 64.6 | |
| 386 | 62.4 | |
| TikTok | 203 | 32.8 |
| YouTube | 386 | 62.4 |
| X (former Twitter) | 95 | 15.3 |
| 132 | 21.3 | |
| Snapchat | 7 | 1.1 |
| Total number of accounts on social media | ||
| 1 | 111 | 17.9 |
| 2 | 216 | 34.9 |
| 3 | 153 | 24.7 |
| 4 | 106 | 17.1 |
| 5 | 15 | 2.4 |
| 6 | 18 | 2.9 |
| Daily time spent on social media, mean, standard deviation | 2.7 | 1.6 |
| Frequency of posting on social media | ||
| Never | 209 | 33.8 |
| About once a month | 252 | 40.7 |
| About once in two weeks | 101 | 16.3 |
| About once a week | 38 | 6.1 |
| Two-four days a week | 16 | 2.6 |
| Five-seven days a week | 3 | 0.5 |
Table 3.
Political engagement of the participants.
| Political engagement | N | % |
|---|---|---|
| How closely do you follow news about politics? | ||
| Not closely at all | 8 | 1.3 |
| Not very closely | 79 | 12.8 |
| Somewhat closely | 247 | 39.9 |
| Closely | 202 | 32.6 |
| Very closely | 83 | 13.4 |
| How often do you talk to people about politics? | ||
| Not at all | 34 | 5.5 |
| Rarely | 128 | 20.7 |
| Sometimes | 295 | 47.7 |
| Often | 142 | 22.9 |
| Very often | 20 | 3.2 |
| Composite score for the variable “political engagement”, mean, standard deviation | 6.4 | 1.7 |
Table 4.
Participants’ opinion on social media ban for children.
| Opinion on social media ban for children | Strongly disagree | Disagree | Neutral | Agree | Strongly agree | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Opinion in general | N | % | N | % | N | % | N | % | N | % |
| To what extent do you agree with the fact that the problematic social media use among children constitutes an important public health concern? | 0 | 0 | 10 | 1.6 | 24 | 3.9 | 263 | 42.5 | 322 | 52.0 |
| To what extent do you agree with the implementation of a social media ban for all children under age of 15? | 22 | 3.6 | 58 | 9.4 | 111 | 17.9 | 279 | 45.1 | 149 | 24.1 |
| Do you need more information from the government regarding the implementation of the ban? | 0 | 0 | 10 | 1.6 | 35 | 5.7 | 415 | 67.0 | 159 | 25.7 |
| Do you believe that a social media ban may lead parents to a false sense of security regarding their children’s safety, resulting in reduced engagement with their children’s digital education? | 54 | 8.7 | 171 | 27.6 | 218 | 35.2 | 132 | 21.3 | 44 | 7.1 |
| Do you believe that additional measures, beyond a social media ban, should be implemented to address the problem? | 4 | 0.6 | 15 | 2.4 | 65 | 10.5 | 365 | 59.0 | 170 | 27.5 |
| Do you believe that a social media ban violates children’s rights? | 124 | 20.0 | 318 | 51.4 | 127 | 20.5 | 43 | 6.9 | 7 | 1.1 |
| Confidence in the effectiveness of the social media ban | ||||||||||
| Children will be able to find ways to create accounts on platforms to which the social media ban will be applied | 7 | 1.1 | 20 | 3.2 | 44 | 7.1 | 335 | 54.2 | 213 | 34.4 |
| Children will create accounts on platforms that will be free and, therefore, less regulated | 14 | 2.3 | 20 | 3.2 | 48 | 7.8 | 311 | 50.2 | 226 | 36.5 |
| Social media platforms will fully comply with the legislation regarding the social media ban | 32 | 5.2 | 162 | 26.2 | 225 | 36.3 | 165 | 26.7 | 35 | 5.7 |
| Impact of the social media ban on children’s lives | ||||||||||
| Social media ban will improve children’s mental health | 35 | 5.7 | 76 | 12.3 | 226 | 36.5 | 232 | 37.5 | 50 | 8.1 |
| Social media ban will improve children’s sleep quality | 21 | 3.4 | 50 | 8.1 | 127 | 20.5 | 300 | 48.5 | 121 | 19.5 |
| Social media ban will improve children’s school performance | 10 | 1.6 | 66 | 10.7 | 249 | 40.2 | 232 | 37.5 | 62 | 10.0 |
| Parental familiarity with digital parental control tools | ||||||||||
| Are you familiar with and able to use available tools and applications that enable parental control over children’s access to social media?a | 18 | 9.0 | 23 | 11.6 | 82 | 41.2 | 64 | 32.2 | 12 | 6.0 |
| Are you familiar with and able to use the application “Kids Wallet”?a | 92 | 46.2 | 32 | 17.6 | 28 | 14.1 | 21 | 10.6 | 23 | 11.6 |
a Only parents of children aged 8-17 years were included in this analysis.
Table 5.
Multivariable linear regression model with score on the variable “confidence in the effectiveness of the social media ban” as the dependent variable.
Table 5.
Multivariable linear regression model with score on the variable “confidence in the effectiveness of the social media ban” as the dependent variable.
| Independent variables | Adjusted coefficient beta | 95% CI for beta | P-value | VIF |
|---|---|---|---|---|
| Females vs. males | 0.276 | -0.048 to 0.599 | 0.095 | 1.110 |
| Age | 0.013 | 0.001 to 0.026 | 0.040 | 1.263 |
| Parents with children aged 8-17 years | 0.104 | -0.269 to 0.477 | 0.584 | 1.288 |
| College degree vs. high school | 0.118 | -0.234 to 0.470 | 0.510 | 1.298 |
| Financial status | -0.084 | -0.182 to 0.014 | 0.094 | 1.101 |
| Total number of accounts on social media | 0.064 | -0.075 to 0.204 | 0.364 | 1.185 |
| Daily time spent on social media | 0.019 | -0.092 to 0.130 | 0.734 | 1.405 |
| Frequency of posting on social media | -0.031 | -0.193 to 0.132 | 0.711 | 1.180 |
| Political engagement | -0.084 | -0.183 to 0.014 | 0.093 | 1.131 |
Adjusted R2 for the final multivariable model = 1.5%; p-value for ANOVA = 0.033. CI: confidence interval; VIF: variance inflation factor.
Table 6.
Multivariable linear regression model with score on the variable “impact of the social media ban on children’s lives” as the dependent variable.
Table 6.
Multivariable linear regression model with score on the variable “impact of the social media ban on children’s lives” as the dependent variable.
| Independent variables | Adjusted coefficient beta | 95% CI for beta | P-value | VIF |
|---|---|---|---|---|
| Females vs. males | 1.528 | 1.154 to 1.902 | <0.001 | 1.110 |
| Age | 0.036 | 0.021 to 0.050 | <0.001 | 1.263 |
| Parents with children aged 8-17 years | 0.075 | -0.356 to 0.507 | 0.731 | 1.288 |
| College degree vs. high school | 0.446 | 0.039 to 0.853 | 0.032 | 1.298 |
| Financial status | 0.154 | 0.041 to 0.268 | 0.008 | 1.101 |
| Total number of accounts on social media | 0.189 | 0.028 to 0.351 | 0.021 | 1.185 |
| Daily time spent on social media | 0.187 | 0.059 to 0.316 | 0.004 | 1.405 |
| Frequency of posting on social media | 0.035 | -0.153 to 0.222 | 0.716 | 1.180 |
| Political engagement | 0.113 | -0.001 to 0.227 | 0.051 | 1.131 |
Adjusted R2 for the final multivariable model = 18.1%; p-value for ANOVA < 0.001. CI: confidence interval; VIF: variance inflation factor.
Table 7.
Multivariable linear regression model with score on the variable “parental familiarity with digital parental control tools” as the dependent variable.
Table 7.
Multivariable linear regression model with score on the variable “parental familiarity with digital parental control tools” as the dependent variable.
| Independent variables | Adjusted coefficient beta | 95% CI for beta | P-value | VIF |
|---|---|---|---|---|
| Females vs. males | 0.058 | -0.500 to 0.616 | 0.837 | 1.059 |
| Age | -0.060 | -0.108 to -0.011 | 0.017 | 1.116 |
| College degree vs. high school | -0.039 | -0.721 to 0.643 | 0.910 | 1.211 |
| Financial status | 0.197 | -0.004 to 0.398 | 0.054 | 1.184 |
| Total number of accounts on social media | 0.571 | 0.343 to 0.799 | <0.001 | 1.307 |
| Daily time spent on social media | 0.080 | -0.174 to 0.333 | 0.536 | 1.699 |
| Frequency of posting on social media | 0.014 | -0.310 to 0.339 | 0.931 | 1.730 |
| Political engagement | 0.075 | -0.122 to 0.328 | 0.426 | 1.352 |
Adjusted R2 for the final multivariable model = 20.7%; p-value for ANOVA < 0.001. CI: confidence interval; VIF: variance inflation factor.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
