Adolescent Preferences for Vlogs and Access to Restricted Content

Adolescent Preferences for Vlogs and Access to Restricted Content | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adolescent Preferences for Vlogs and Access to Restricted Content Smaranda Cioban Kudelca, Denisa Dobai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7048141/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The digital space provides a distinct environment in which today's adolescents can engage in various activities, including watching diverse content and interacting with friends. However, challenges arise when the materials or content accessed are not age-appropriate. Watching vlogs has become a particularly significant activity in adolescents' lives. This article highlights the preferences of Romanian adolescents for vloggers and their exposure to restricted content on the YouTube platform. The present study is based on a sample of 2,558 eighth-grade students from Bihor County, Romania, who participated in a survey conducted in 2018. The methodological approach combines social network analysis to identify vlogger micro-communities and sequential multinomial logistic regression to assess the impact of sociodemographic factors, attitudes, and behaviors on the consumption of restricted content. The findings indicate that adolescents tend to favor vloggers who post restricted content, with significant correlations to variables such as gender, maternal education level, risk attitudes, and engagement in unstructured digital socialization. Vloggers from popular communities are more frequently associated with restricted content, and students with higher academic performance exhibit a greater likelihood of accessing such content. Sociology vlogs adolescents online preferences restricted content online risk 1. Introduction In the digital era, online platforms play a central role in adolescents' daily lives, shaping both how they allocate and structure their leisure time and how they manage social relationships and shape cultural preferences (Smith, Hewitt, & Skrbis, 2015 ). The digital space has become increasingly dominant, being recognized as a distinct social framework where users can construct their identities and interact based on shared interests (Goffman, 1978 ; McLuhan, 1994 ; Meyrowitz, 1986 , 1997 ). In this context, YouTube stands as the most widely used content-sharing platform globally, enabling the uploading, viewing, and dissemination of a vast array of content (Figueiredo, Benevenuto, & Almeida, 2011 ). For adolescents, subscribing to specific channels, watching, and sharing preferred content serve as key forms of leisure and socialization. Examining vlog and vlogger preferences is therefore essential for understanding the online experiences of adolescents and identifying the potential risks to which they are exposed. The YouTube platform ranks at the top of the preferences of 8th-grade students in Bihor County, as evidenced by the Omnibus 2022 research (Bihor, Oradea, & Bihor, 2022 ). As Dynel ( 2014 ) points out, communication on the YouTube platform encompasses three levels: video interaction, the interpretation of YouTube video discourse, and the interpretation of video comments. From this perspective, a person can simultaneously act as a content creator (vlogger), a content consumer (subscriber), and a content critic (with the ability to interpret received comments). This study outlines the profile of adolescents exposed to restricted content, namely those video materials categorized by the YouTube platform as unsuitable for minors. The research aims to examine Internet usage among adolescents, focusing on vlogging, and to classify respondents based on their preferred vloggers. At the same time, it is based on the premises of the uses and gratifications theory (Alan, 2009 ; Ruggiero, 2000 ; So, 2012 ), which posits that users select content according to their preferences. While emphasizing that users choose the content they engage with, content itself plays a significant role in shaping human attitudes and behaviors, as argued by cultivation theory scholars (Morgan, Shanahan, & Signorielli, 2017 ; Morgan, Shanahan, Signorielli, & Michael Morgan, 2014; Stefanone, Lackaff, & Rosen, 2010 ). Therefore, it is highly likely that the choice of vloggers influences adolescents' attitudes and behavior. The primary aim of this study is to identify the predictors of adolescents' consumption of restricted vlog content. In terms of the paper's objectives, the focus is on analyzing the micro-communities of vloggers referenced in the MERPAS 2018 Survey and identifying the most influential vloggers, based on the preferences expressed by students. Additionally, the study seeks to determine the predictors and correlates of adolescent consumption of vloggers, including those targeting a general audience and those posting restricted content. 2. Literature Review Since the 2000s, Romanian adolescents have rapidly integrated the internet into their daily routines. As a result, the online environment represents their primary, or even sole, means of spending their leisure time (Ştefănescu, 2008 ). Compared to their peers in other European countries, Romanian adolescents have reported higher daily internet usage but significantly lower scores regarding the diversity of activities they engage in online (Balea, 2016 ; Mascheroni et al., 2013 ; Ragnedda & Muschert, 2013 ; Smahel et al., 2020 ). The prevalence of internet use among adolescents, driven by mobile phone access and challenges in parental monitoring, has made the online space key for adolescents to express their identity (Balakrishnan & Griffiths, 2017 ; Chiang & Hsiao, 2015 ; Montes-Vozmediano, García-Jiménez, & Menor-Sendra, 2018 ; Morris & Anderson, 2015 ; Pérez-Torres, Pastor-Ruiz, & Abarrou-Ben-Boubaker, 2018 ) and connect with peers from offline or online life (Velicu & Marinescu, 2019 ). In this context, the online-offline dynamic and the examination of how online interactions influence the offline environment have become a focal point in scientific research (Symons, Vanwesenbeeck, Walrave, Van Ouytsel, & Ponnet, 2020 ). Digital interactions involve constructing a self-projection and digital identity (Camacho, Minelli, & Grosseck, 2012 ), with varying awareness among adolescents about how online content shapes their image. In the study of vlog-type content, particular attention is given to video materials deemed inappropriate for minors (Beers Fägersten, 2017 ; Döring & Mohseni, 2020 ; Hattingh, 2021 ; Khasawneh et al., 2020 ). Such content is classified by the YouTube community under the generic term "restricted content" (YouTube, 2021 ) and includes the following categories: horror, hate speech, videos depicting dangerous behaviors, promoting discriminatory situations, encouraging self-harm, self-mutilation, or eating disorders (including extreme dieting), adult content (nudity and sexual content), and offensive or abusive language. Once YouTube’s algorithms, platform moderators, or users report the presence of such content in a vlog, it is classified as restricted and becomes inaccessible to certain age groups (e.g., individuals under 18 years old). For the purposes of this study, restricted vlogs are categorized as restricted online content. It is crucial to acknowledge that the online environment under discussion is governed by general norms and regulations designed to guide user behavior. In this regard, the concepts of "deviant online behavior" and "restricted online content" are central to the present study. In this context, "deviant online behavior" encompasses various forms of social rule violations within the digital space (Barbovschi, Green, & Vandoninck, 2013 ; Mascheroni & Ólafsson, 2014 , 2016 ; Schrock & Boyd, 2008 ; UNICEF, 2019 ; Velicu, Balea, & Barbovschi, 2019 ; Yesilada & Lewandowsky, 2021 ). Meanwhile, restricted content refers to digital materials deemed inappropriate for specific age groups (particularly children and adolescents) due to their potentially harmful effects on behavior (Alshamrani, 2020 ; Barrientos, Alaiz-Rodríguez, González-Castro, & Parnell, 2020 ; Boyd, Ryan, & Leavitt, 2011 ; Kaushal, Saha, Bajaj, & Kumaraguru, 2016 ; Liu et al., 2014 ; Mothe, Parikh, & Ramiandrisoa, 2020 ; Schrock & Boyd, 2008 ; Suryawanshi, Chakravarthi, Arcan, & Buitelaar, 2020 ; Velicu, Balea, et al., 2019). Accessing such restricted materials in the online space is considered a risky behavior in relation to Internet usage (Velicu, Balea, et al., 2019). Online risks are closely associated with problematic behaviors in the digital environment, which can adversely impact the development, value systems, and both the physical and psychological well-being of users, particularly adolescents. Concurrently, online risks are juxtaposed with online opportunities, which include content that facilitates developmental processes and socialization among users (Livingstone, 2016 ; OECD, 2021 ; Schrock & Boyd, 2008 ). To understand deviant behavior and the consumption of restricted content, the theory of power and control provides an essential analytical framework. This theory emphasizes the role of factors such as gender, social status, attitude toward risk, parental supervision, and affiliation with deviant subcultures as key predictors of deviant behavior. Furthermore, the theory highlights the influence of familial cultural values, which have the capacity to shape attitudes and behaviors among younger generations (Hagan, 1991 ; Hagan & Foster, 2001 ; Hagan, Gillis, & Simpson, 1985 ). Another essential theoretical framework for analyzing restricted content is the opportunity and routine activity theory (Osgood, Johnston, Omalley, & Bachman, 1988 ; Osgood, Wilson, Omalley, Bachman, & Johnston, 1996 ; Vazsonyi, Javakhishvili, & Ksinan, 2018 ). This theory focuses on the concept of unstructured socialization, defining it as the act of spending leisure time without clear direction, specific objectives, alongside peer groups, and without adult supervision (Osgood et al., 1996 ). Unstructured socialization is considered as a potential risk for norm violation and the development of deviant behaviors, as it is associated with rewards such as alleviating boredom, immediate gratification, and integration into group hierarchies, as well as peer pressure. Moreover, the opportunity and routine activity theory can also be applied to analyze deviant behaviors in the online environment. Thus, unstructured socialization in the digital space involves activities such as browsing the Internet, accessing social networks, and other similar behaviors that may contribute to the emergence of deviant acts, including cyberbullying or cyberstalking (Marcum & Higgins, 2021 ; Marcum, Higgins, & Ricketts, 2010 ; Mesch, 2009 ; Navarro & Jasinski, 2012 ). 3. Methodology This research focuses on Internet usage and vlogging preferences among eighth-grade students from Bihor County, based on the MERPAS Educational Survey conducted in 2018. The survey targeted eighth-grade students enrolled in mainstream education during the 2018–2019 academic year, with 4333 valid responses. Of these, 2558 students reported following vlogger content, with a slightly higher proportion of males (51.6%, 1319 students). Data was collected via a Google Forms questionnaire, including a question about vlogging consumption. Students listed their most popular vloggers, and the names were standardized by replacing nicknames with official ones. A total of 997 unique vloggers were mentioned, and their channels were analyzed to identify the number of restricted videos posted on YouTube. Connections between vloggers were established based on shared selections by respondents, allowing for network analysis in Gephi to identify key influencers and micro-communities. The study also examined the type of content accessed by adolescents by analyzing restricted videos posted by the mentioned vloggers from channel creation until November 1, 2018. This analysis, conducted in April-May 2022, covered approximately 900 channels. For statistical analysis, vloggers were categorized by the percentage of restricted content on their channels. A categorical variable was created to distinguish students who do not follow vlogs, those who follow general-audience vloggers, and those who follow vloggers posting restricted content. The presence of restricted content was used as an indicator of exposure to potentially inappropriate material. In order to achieve the aforementioned objectives (analysis of the vloggers' micro communities and identification of the most influential vloggers, as well as the analysis of predictors of vlog consumption among adolescents), sequential multinomial logistic regression was applied. 3.1. Network analysis and the modularity procedure The modularity function (Danon, Díaz-Guilera, Duch, & Arenas, 2005 ) was used to identify hidden patterns by measuring the internal vs. external connections of communities, assuming that entities are more connected within groups than outside them. Network visualization was performed using the Force Atlas 2 and ForceAtlas algorithms (Bastian, Heymann, & Jacomy, 2009 ; Jacomy, Venturini, Heymann, & Bastian, 2014 ). which balance attraction and repulsion forces to structure the network. The Force Atlas algorithm brings less connected entities closer to highly connected ones. Model parameters included a repulsion strength of 5000.0, attraction strength of 5.0, and gravity 80 to emphasize micro-communities. The Label Adjust procedure (Bastian et al., 2009 ) was applied to prevent overlapping labels and improve readability. 3.2. Identifying the presence of restricted content posted by vloggers Identifying restricted content on vlog channels mentioned by students involved querying each video’s rating from the channel's creation until the MERPAS 2019 Survey. The 967 vlog channels were processed to determine posted videos, then evaluated using Google's API (Application Programming Interface) to extract YtRating, YouTube’s content restriction indicator. A restricted content score was calculated for each channel based on the proportion of restricted videos. Each student’s score was computed by summing the percentages of restricted content from the vloggers they followed. This score was analyzed in relation to vlog preferences and the number of vloggers followed through correlation analysis. The variable was dichotomized due to a leptokurtic distribution. Bivariate analyses (cross-tabulations and Chi-Square tests) assessed associations between community preferences and restricted content. Students who did not follow any vlogs were included, and a new variable was created combining restricted content presence and vlog consumption. 3.3. Multivariate analysis of the profile of students vulnerable to watching restricted vlogs The profile of students vulnerable to restricted content was analyzed using sequential multinomial logistic regression. Before testing the model, assumptions and multicollinearity were assessed, with VIF values (max 1.633) and Hosmer & Lemeshow tests confirming validity. School characteristics significantly influenced vlog preferences, as shown by the ICC values: 0.03 (restricted vs. general audience), 0.15 (restricted vs. no vlogs), and 0.06 (general audience vs. no vlogs). Due to this limitation, a sequential multinomial logistic model was estimated with five blocks : Block I : Control variables (sociodemographic variables: gender, mother's education, parents' residential environment, and socioeconomic asset index). Block II : Adds school performance (general grade average from the previous year). Block III : Attitudinal variables (attitude toward risk, preference for a party lifestyle and the student's perception of parental monitoring). Block IV : Online behavior variables, specifically the frequency of digital device use (operationalized by the factorial score of time spent on smartphones and computers) and unstructured digital socialization (operationalized using smartphones and tablets for entertainment and socialization). Block V : Examines content type and deviant behavior (involvement in problematic school behaviors). In consideration of the aforementioned factors, the study posits the following theoretical hypothesis: The probability of an adolescent engaging with vloggers targeting a general audience or those posting restricted content is influenced by their attitude toward risk, parental monitoring, school results, adoption of a hedonistic and party-oriented lifestyle, time spent online, preference for unstructured digital socialization activities, and an increase in engagement with problematic school behaviors. 3.4. Research hypotheses The theoretical hypotheses were tested through operational hypotheses based on a sequential multinomial logistic regression (in blocks ), in order to assess the actual effect (while controlling for the effect of other variables included in the model). The main hypotheses are: • Hypothesis 1. The probability of following vloggers who post restricted content is higher for adolescents with a more open attitude toward risk-taking. • Hypothesis 2. A lower level of parental monitoring is associated with an increased probability of following vloggers who post restricted content. • Hypothesis 3. Students with poorer academic performance are more likely to follow vloggers who post restricted content. • Hypothesis 4. The adoption of a lifestyle focused on entertainment and a party-oriented leisure activity increases the likelihood of belonging to the group of adolescents who follow restricted-content vlogs, compared to the group of students who do not follow vloggers. • Hypothesis 5. The more time adolescents spend online, the higher the likelihood that they will follow vloggers who post restricted content. • Hypothesis 6. The preference for unstructured digital socialization, operationalized through the extent of use of electronic devices for socializing and entertainment, increases the chance of following vloggers who post restricted content. • Hypothesis 7. Engaging in problematic school behaviors increases the chance of belonging to the category of students who watch content-restricted vlogs. 4. Results and discussions Based on the theoretical model presented, which investigates exposure to restricted content from the perspective of classical approaches to the study of deviance (theories of power and control, opportunity and routine activities theory), a model was estimated to assess the influence of predictors identified in the relevant literature on a specific adolescent behavior, namely, following vloggers who post restricted content. 4.1. Preliminary analyses. Network of students following vloggers A bipartite network of 3,157 nodes (entities) and 5,406 edges (connections) was constructed to map students' vlog preferences. The analysis reveals an average of three connections per entity, a modularity score of 0.642, and 115 identified communities, most of them sparsely connected. The largest communities (named after their main hub) include Selly Class (20.65%), Hungarian Vloggers Class (8.96%), Codrin Bradea Class (8.24%), and MaxInfinite Class (7.51%), among others. 4.2. Distribution and Analysis of Restricted Content Given the high prevalence of restricted content (over 10 vlogs age-restricted before September 2018), this type of content is mainly found among vloggers with few mentions, except for Codrin Bradea (166 mentions), PewDiePie (118), and ZappyTV (32). Most of these vloggers belong to the modularity classes of PewDiePie (42), Codrin Bradea (58), and Hungarian Vloggers (43). The Summative Score for Restricted Content was calculated by aggregating the content ratings of watched channels. Results indicate that students primarily follow vloggers with minimal restricted content, though some also follow those frequently sanctioned, likely due to language use. Table 1 Descriptive statistics for the Summative Restricted Content Score Summative Score of Restricted Content SE (Standard Error) Mean 1.319 0.50 Median 0.62 Variance 5.904 Standard deviation 2.429 Minimum 0 Maximum 38.060 Skewness 5.630 0.05 Kurtosis 57.890 0.10 Table 1 presents the descriptive statistics for the Summative Restricted Content Score. Given that 72% of valid channels (586 out of 807) had no restricted videos during the analyzed period, a dichotomous variable was created to distinguish channels with at least one restricted video from those targeting a general audience. Table 2 Bivariate analysis of the Summative Restricted Content Score Number of vloggers followed Modularity Classes Modularity Classes -0.005 (Spearman) Summative Restricted Content Score 0.205 *** (Pearson Correlation) 0.161 ** (Spearman) Bivariate analysis ( Table 2 ) shows a significant correlation between the Summative Restricted Content Score and modularity classes (Pearson: 0.205***, Spearman: 0.161**). The Chi-Square test ( Table 3 ) confirms a statistically significant association between modularity classes and restricted content (χ² = 177.631, df = 19, p < 0.0001). Preferences for certain communities (Codrin Bradea, PewDiePie, Vlad Munteanu, Selly, Logan Paul) are linked to restricted content, while others (Andreea Balaban, ACE Family, Andra Gogan, GajuKyd, Hungarian vloggers) show a lower likelihood of such content. Table 3 Association between the presence of restricted content and modularity classes Value Df Asymptotic Significance (2-sided) Pearson Chi-Square 177.631 a 18 .000 Likelihood Ratio 178.822 18 .000 Linear-by-Linear Association 7.100 1 .008 N of Valid Cases 2016 4.3. Analysis of the profile of users engaging with restricted content Network analysis indicates that students who watch restricted content also follow general-audience vloggers, forming micro-communities. Vloggers with at least one restricted video appear in all modularity classes with at least four entities. While most vloggers have not posted restricted content, all communities include at least one who has, amplifying their influence. The summative score for restricted content correlates weakly but directly with modularity classes and the number of vloggers followed, suggesting that students who follow more vloggers are more likely to engage with restricted content. The Chi-Square test confirms this association, highlighting modularity classes with a higher presence of restricted content (Codrin Bradea, PewDiePie, Vlad Munteanu, Selly, Logan Paul) and those linked to general-audience content (Andreea Balaban, ACE Family, Andra Gogan, Hungarian vloggers, Gajukyd, MaxInfinite). Given adolescents’ varied exposure to restricted content, further analyses were conducted to profile those most vulnerable, considering sociodemographic factors. Since the dependent variable is categorical (with three categories), sequential multinomial logistic regression (in blocks ) was used to compare the impact of independent variables on the categories of the dependent variable, relative to the reference category. Predicted logit (Y1) = Intercept (Constant) + β1 + β2 + β3 + β..+ e (1) Predicted logit (students who follow general audience vloggers vs. students who follow vloggers with restricted content): Significant predictors include sex, mother’s education, academic results, attitude toward risk, and unstructured digital socialization. Predicted logit (students who do not follow any vlog vs. students who follow vloggers with restricted content): Key predictors are sex, mother’s education, academic results, attitude toward risk, and unstructured digital socialization. At first glance, the estimated models demonstrate an improvement in fit compared to the previous model, with the greatest contribution to predicting the likelihood of belonging to the reference category observed in Block III . This block includes predictors such as attitude toward risk, leisure oriented toward entertainment and parties, and parental monitoring. These factors play a significant role in explaining the likelihood of belonging to the reference category of students following vloggers with restricted content. Table 4. Statistics Regarding Model Fit Block Model Fitting Criteria Likelihood Ratio Tests Model -2 Log Likelihood Chi- Square AIC BIC df Sig. Block 1 Intercept Only 627.215 638.041 623.215 Final 542.059 596.187 522.059 101.156 8 .000 Block 2 Intercept Only 2350.066 2360.891 2346.066 Final 2248.960 2313.913 2224.960 121.105 10 .000 Block 3 Intercept Only 3515.474 3526.299 3511.474 Final 3358.467 3455.897 3322.467 189.006 16 .000 Block 4 Intercept Only 3515.474 3526.299 3511.474 Final 3329.862 3448.943 3285.862 225.611 20 .000 Block 5 Intercept Only 3351.711 3362.450 3347.711 Final 3166.306 3295.176 3118.306 229.405 22 .000 Table 5 Statistics Regarding Goodness of Fit Goodness of Fit Chi-Square df Sig. Block 1 Pearson 170.661 144 .064 Deviance 186.630 144 .010 Block 2 Pearson 1697.834 1670 .312 Deviance 1787.369 1670 .023 Block 3 Pearson 3313.987 3288 .372 Deviance 3322.467 3288 .333 Block 4 Pearson 3303.371 3286 .412 Deviance 3285.862 3286 .497 Block 5 Pearson 3166.235 3144 .387 Deviance 3118.306 3144 .624 Table 6 Pseudo R-Square Variables Pseudo R-Square Block 1 Block 2 Block 3 Block 4 Block 5 Cox and Snell .059 .070 .108 .127 .135 Nagelkerke .067 .080 .123 .145 .153 McFadden .029 .034 .054 .064 .069 The final model provides a better explanation of the probability of belonging to a specific category of students (those following vloggers posting restricted content) compared to the baseline model with only the constant. Model fit statistics show significant improvements (Chi-Square = 229.405***, df = 22), with Pseudo R-Square values indicating a better model fit in Block 5 . As demonstrated in Table 4 , the model fit statistics show improvements across the blocks , with significant differences in Chi-Square values (p < .000) as the number of predictors increases. In Table 5 , the goodness-of-fit statistics show a Pearson Chi-Square value of 3166.235 (df = 3144, p = 0.387), indicating an adequate model fit, while Table 6 presents the Pseudo R-Square values, which demonstrate an improvement from Block 1 to Block 5 . 4.4. Analysis of the Impact of Key Predictors In the present analysis, the significant results are supported by data from Tables 7 and 8 (see Appendix 1 and 2 ), which illustrate key predictors of adolescent vlog content consumption, including parental education, lifestyle, risk attitude, and digital socialization (Table 7 ). Table 8 also highlights the correlation between academic performance and the type of content followed. The final model identifies key predictors of following vloggers who post restricted content, including gender, maternal education, academic achievement, attitude toward risk, party lifestyle, unstructured digital socialization, and frequency of device use for internet access. Significant predictors include unstructured digital socialization (Chi-Square = 31.981, p < 0.0001), maternal education (Chi-Square = 25.425, p < 0.0001), risk attitude (Chi-Square = 22.360, p < 0.0001), and academic achievement (Chi-Square = 18.147, p < 0.0001). Gender and party lifestyle also contribute significantly. Boys are more likely to follow restricted content vloggers, while girls prefer general content (B = -0.469, Exp(B) = 0.625, p = 0.002). Students whose mothers completed at least lower secondary education are more likely to follow restricted content vloggers (B = 0.825, Exp(B) = 2.282, p < 0.0001). A pro-risk attitude also increases the likelihood of following restricted content vloggers (B = -0.348, Exp(B) = 0.706, p < 0.0001). However, no significant difference in risk attitude was found between those following restricted versus general content vloggers. Regarding the formulated hypotheses, the first hypothesis suggests that a pro-risk attitude increases the likelihood of exposure to restricted content among adolescents, supported by previous studies (Khurana et al., 2019 ; Nesi et al., 2021 ; Valkovičová, 2021 ). However, no significant difference was found between the likelihood of following general-audience vs. restricted content vloggers. This aligns with Haridakis and Hanson ( 2009 ), who note that a risk-taking attitude predicts YouTube content consumption, but the effect diminishes when content-watching motives are controlled. Both groups of followers, general-audience and restricted content, share a pro-risk attitude. The second hypothesis examines the role of parental monitoring in predicting the following of vloggers with restricted content. The results do not support this hypothesis, as no evidence suggests that students with less parental monitoring are more likely to access inappropriate vlogs. This contrasts with findings in the literature on problematic internet use and online victimization (Brighi, Menin, Skrzypiec, & Guarini, 2019 ). A meta-analysis on the relationship between social media use and parent-child relationships found weak negative correlations between both general upbringing and specific parental control, including monitoring (Lukavská, Hrabec, Lukavský, Demetrovics, & Kiraly, 2022 ). Parental monitoring does moderate the relationship between restricted content exposure and alcohol consumption, though its impact is weak and lacks long-term effects (Smout et al., 2021 ). Online mediation styles also influence social media use (Beyens, Keijsers, & Coyne, 2022 ; Geurts, Koning, Vossen, & van den Eijnden, 2022 ; Page Jeffery, 2021 ). Research shows that instructive parental mediation reduces the risk of online victimization (Rega, Gioia, & Boursier, 2022 ; Wachs, Costello, et al., 2021 ), with gender differences observed in parental monitoring (Wallace, 2021 ). Thus, girls are asked more frequently about their social media activity than boys, providing support for the hypothesis of gender differences in parenting and behavior, as posited by control and power theorists (Hadjar, Baier, Boehnke, & Hagan, 2007 ). The inconsistency of findings regarding the impact of parental monitoring in relation to exposure to restricted content, as documented in the literature, aligns with the results obtained. The third hypothesis , regarding the negative association between academic performance and following vloggers with restricted content, is rejected. Academic performance is positively associated with following restricted content vloggers (B = -0.224, Exp(B) = 0.799, p = 0.001), contrary to expectations. This suggests that better academic performance may lead to greater permissiveness regarding internet use, particularly for boys. Despite parental concerns about the impact of social media on school performance (Velicu, Chaudron, Dias, Brito, & Lobe, 2019 ), literature shows insufficient evidence to support this hypothesis (Astatke, Weng, & Chen, 2021 ; Marker, Gnambs, & Appel, 2018 ; Sampasa-Kanyinga, Chaput, & Hamilton, 2019 ). Additionally, restricted content vloggers often share gaming content in English, suggesting that English proficiency may explain the academic performance differences between students who follow general vs. restricted content vloggers. The fourth hypothesis , which posits a link between a party-oriented lifestyle and the likelihood of following vloggers with restricted content, is supported. The final model shows that students with a disposition towards a party lifestyle are more likely to follow restricted content vloggers compared to those who do not follow any vlog channels (B = -0.174, Exp(B) = 0.840, p = 0.033). However, no significant difference was found between followers of restricted and general content vloggers in terms of party lifestyle (B = 0.131, Exp(B) = 1.139, p > 0.1). The findings are consistent with the literature, which reveals that a party-oriented lifestyle and the posting of photos and videos with individuals enjoying parties are valued by adolescents who participate in club and nightclub outings (Kavanaugh & Anderson, 2017 ; Pavón-Benítez, Romo-Avilés, & Sánchez-González, 2021 ). The adoption of a party-oriented lifestyle is influenced by dispositional factors, affected by social status (Calderon Gomez, 2021 ; Fornari, 2019 , 2020 ; Hatos, 2007 ; Murdock, 2010 ; North, Snyder, & Bulfin, 2008 ; Robinson, 2009 ; Sheldon, Antony, & Sykes, 2020 ; Webster, 2020 ; Yates, Kirby, & Lockley, 2015 ), and active participation in parties and other social activities is a risk factor related to intensive social media use (Sheldon et al., 2020 ). At the same time, the presence of social and dispositional factors influencing the use of the YouTube platform has been documented in the literature (Balakrishnan & Griffiths, 2017 ; Haridakis & Hanson, 2009 ; Taylor & Cingel, 2021 ). However, no differences were found in the preference for a party-oriented lifestyle between followers of general and restricted content vlogs. These findings support the opportunity and routine activity theory, as well as the control and power theory, linking subcultural preferences with behavior (Hagan, 1991 ; Hatos, 2007 ). Statistically significant differences were found between students who follow restricted vlogs and those who do not, with the former using the Internet more frequently (B = -0.126, Exp(B) = 0.882, p = 0.059). No significant differences were observed between those who follow restricted and general audience vlogs (B = -0.048, Exp(B) = 0.953, p > 0.5). These findings partially confirm the fifth hypothesis , that increased online time is linked to a higher likelihood of following restricted content, as supported by previous research (Laconi et al., 2018 ). Additionally, time spent online leads to a proportional increase in exposure to restricted content (Livingstone, 2013 ; Livingstone, Ólafsson, et al., 2017 ; Jan Van Dijk & Hacker, 2003 ; Velicu, Balea, et al., 2019; Velicu & Marinescu, 2019 ; Wachs, Mazzone, et al., 2021 ). Time spent online is also linked to greater exposure to both risks and opportunities in the digital environment (Bedrosova, Machackova, Šerek, Smahel, & Blaya, 2022 ; Demeter, 2020 ; Diaconescu, Barbovschi, & Baciu, 2008 ; Livingstone, Mascheroni, & Stoilova, 2021 ; Mascheroni & Ólafsson, 2016 ; Oksanen, Hawdon, Holkeri, Nasi, & Rasanen, 2014 ; Velicu, Balea, et al., 2019) and mediates the relationship between problematic use and parental monitoring (Brighi et al., 2019 ). The time spent online does not significantly differentiate between following general vs. restricted vlogs. However, differences are found between boys from wealthier families who follow restricted vlogs and girls from lower socioeconomic status families who do not follow any vlog channel, suggesting a digital divide (DiMaggio & Garip, 2012 ; DiMaggio & Hargittai, 2001 ; DiMaggio, Hargittai, Celeste, & Shafer, 2004 ; Hatos, 2020 ; Van Deursen & Van Dijk, 2014 ; Jan Van Dijk, 2020 ). The probability of following vloggers who post restricted content is directly related to phone/tablet use for entertainment and socializing. Students who follow restricted content vloggers use their phones more often for social media and entertainment compared to those who follow general audience vloggers (B = -0.208, Exp (B) = 0.813, p = 0.014) and those who do not follow any vlog (B = -0.394, Exp (B) = 0.675, p < 0.0001). These findings support the sixth hypothesis , which tests the relationship between the type of online activities and the degree of exposure to inappropriate content for minors. The results are consistent with studies in the field (Laconi et al., 2018 ; Livingstone, Davidson, Batool, Ciaran, & Anulekha, 2017 ; Mascheroni & Ólafsson, 2016 ; Sanders et al., 2022 ; Smout et al., 2021 ). Adolescents who primarily use smartphones for socializing and entertainment are more vulnerable to exposure to restricted content on YouTube. Students spending more time on social media and entertainment are more likely to follow vloggers who post restricted content, confirming the familiarity effect and the bond formed through informal language (Beers Fägersten, 2017 ). The existence of mechanisms that create attachment to specific vloggers and a sense of belonging to a community of users sharing similar interests is akin to the adoption of deviant subcultures, as documented by Hagan ( 1991 ). From the perspective of opportunity and routine activity theory, unstructured digital socialization is one of the main predictors of online deviance (Marcum et al., 2010 ; Navarro & Jasinski, 2012 ; Reyns, Henson, & Fisher, 2011 ; Wachs, Costello, et al., 2021 ; Wachs, Mazzone, et al., 2021 ; Wolfe, Marcum, Higgins, & Ricketts, 2016 ), and constitutes as an equivalent to unstructured socialization. Social media platforms represent a space where adolescents can plan activities in the absence of a responsible guardian (Meldrum & Clark, 2015 ; Pyrooz, Decker, & Moule, 2013 ). The seventh hypothesis suggests a positive relationship between following restricted content on YouTube and engaging in problematic school behaviors, but the results do not support this. Students who access restricted vlogs do not differ significantly from those who follow general audience vlogs or no vlogs in terms of problematic school behaviors. The interpretation of the results regarding the profile of students who follow restricted vlogs indicates similarities with the profile of adolescents who violate copyright laws, as studied by Hagan and Kay ( 1990 ) from the perspective of power and control theory. These students are typically boys from higher social-status families, perform well academically, have access to Internet-connected devices, display a pro-risk-taking attitude, and identify with youth culture. Watching restricted vlogs also serves as a form of socialization, teaching workplace behaviors (e.g., gaming, foul language, irony) and connecting adolescents from wealthy families with future labor market positions. 5. Conclusions This study presents the following of restricted vlogs as a specific form of exposure to restricted online content, characteristic of adolescents from higher social-status families, and particularly popular among boys. Classified within the category of online risks (risky online content), the consumption of potentially restricted online content is predicted by attitudes toward risk, a lifestyle inclined toward parties and entertainment, time spent online, and gender. According to the results obtained, girls with a pro-risk attitude, who show openness toward attending parties and spend more time online, perceive themselves as more exposed to potentially restricted content. Furthermore, the findings indicate that students consider other virtual environments where this content is accessed to be more dangerous than social networks and entertainment content. In this regard, the negative association between the use of electronic devices for entertainment/socialization and exposure to potentially restricted content is particularly illustrative. Considering that a party-oriented lifestyle (leisure inclined toward fun and partying) serves as a proxy for unstructured offline socialization, and the use of electronic devices for entertainment/socialization serves as a proxy for unstructured virtual socialization, the result can be interpreted as evidence of the stronger influence of unstructured socialization in offline life, even compared to activities conducted in the virtual environment. Consequently, offline life aspects exert a significant influence on adolescent behavior, including online risk behaviors (exposure to potentially restricted content) and deviant behaviors (violent online interpersonal deviance). The results also suggest the potential presence of compositional effects related to the pro-risk attitude and the adoption of a party-oriented lifestyle. Thus, the incidence of violent online interpersonal deviance and exposure to potentially restricted content shows an increase in schools with a negative school climate and a higher proportion of boys from lower social-status families. A distinct category of restricted content includes images, video sequences, and audio materials classified as inappropriate for minors. This category includes horror content, violent material, sexually explicit content, materials encouraging dangerous behaviors, hate speech, and offensive language. By analyzing the preference for following vlogs labeled by YouTube as inappropriate for minors (restricted vlogs), a profile emerges of adolescents interested in this content, providing a clearer understanding of the phenomenon studied. Thus, boys from educated families, with a pro-risk attitude, and openness to adopting a party-oriented lifestyle, spend considerable time on social media. For these adolescents, digital socialization is more important than offline socialization, and the digital environment is an integral part of their lives and a way of expressing their identity. Accessing restricted content serves as a substitute for engaging in deviant offline behaviors and provides a way to familiarize themselves with behaviors and attitudes valued in the professional environment they will later join. In this regard, a significant portion of the identified restricted vlogs contain content such as gaming, political satire, and stand-up comedy. The presence of significant differences at the school unit level between students who follow restricted content and those who do not follow any vloggers signals the existence of disparities in terms of Internet access and/or compositional effects, which deepen the divide between students who regularly use the Internet and those with occasional access to digital technology. Although the necessary assumptions for the analyses were strictly verified, it is important to consider the various limitations of the study when interpreting the results. Firstly, the presence of a high number of missing values that are not random may influence the results obtained. As such, the multinomial regression models account for only 1,657 cases out of a total of 4,333 valid cases. Regarding the measurement of the variables included in the study, it is important to note that these are self-reports, meaning activities reported by the participating students. In this context, the study includes measurements of perceptions regarding various behaviors, which may be influenced by factors such as social desirability. The evaluation of the presence of restricted content is another aspect that raises questions regarding validity. For example, querying the number of restricted videos on a channel was conducted approximately three years after data collection, which may influence the identification of certain channels/restricted videos (during that period, it is possible that some channels or videos may have been removed by the creators). It is also possible that the presence of restricted content was reported after the completion of the survey. Consequently, some content currently evaluated as restricted may have been considered general audience content during the period when students watched it. The consistency between the restrictions applied to international channels (English vlogs) and Romanian channels is another unknown factor. Furthermore, regarding restricted content, the use of dichotomous variables may influence the results obtained. Since dichotomous variables (which underpinned the categorical variable for restricted content presence) do not allow for the identification of the degree of exposure to deviant content, this study does not differentiate between students who watch vloggers who occasionally/accidentally post restricted content and those who habitually post such videos. The decision to use this type of variable was based on the leptokurtic distribution of the dependent variable, percentage of vloggers who post restricted content, where the values of 0 predominated. The differentiation between students who do not watch any vloggers and those who watch general audience vlogs constitutes another supporting factor for the decision to use the categorical variable for restricted content presence. To ensure an interpretation of the results obtained, multinomial logistic regressions were estimated, where each of the three categories of the dependent variable served as a reference category. 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Sociol Res Online 20(4):71–91. 10.5153/sro.3751 Yesilada M, Lewandowsky S (2021) A systematic review: The YouTube recommender system and pathways to problematic content YouTube (2021) Reguli și politici. Regulile comunității. Retrieved from https://www.youtube.com/howyoutubeworks/policies/community-guidelines/ Additional Declarations The authors declare no competing interests. Supplementary Files Appendix11.xlsx Appendix 1 Appendix2.xlsx Appendix 2 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7048141","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480809586,"identity":"cb3ebaf1-8ad1-4382-ae8b-43f4424c3697","order_by":0,"name":"Smaranda Cioban Kudelca","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYPACZiBmbACRciDugQekaDEGa0kgTguETGwAsfBpkZ92+NmHnzus5RnYDzc+Lqi5kz4/7PBDoC12croN2LUY3E4zntl7Jt2wgSex2XjGsWe5G2+nGQC1JBubHcChRTrBmIG37TBjgwRjmzQP2+HcjbMTQFoOJG7DoUV+dvpnxr9th+0hWv4dTjecnf4BrxaG2znGzEBbEsFagIwEeekc/LYY3M4pZpZtS09uA/llZt9hww3SOQUHEgxw+wXosM2Mb9usbfvZjz98XPDtsDxI5MOHCjs5XFrggA1uL1ilAQHlqPY2kKJ6FIyCUTAKRgIAAK7+YBPDrYzwAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0574-698X","institution":"University of Oradea","correspondingAuthor":true,"prefix":"","firstName":"Smaranda","middleName":"Cioban","lastName":"Kudelca","suffix":""},{"id":480810011,"identity":"ca32b450-98d2-4430-8e86-16329b26de9f","order_by":1,"name":"Denisa Dobai","email":"","orcid":"","institution":"University of Oradea","correspondingAuthor":false,"prefix":"","firstName":"Denisa","middleName":"","lastName":"Dobai","suffix":""}],"badges":[],"createdAt":"2025-07-04 15:31:34","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7048141/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7048141/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86260012,"identity":"93182e84-72c9-4636-8428-199fdc562601","added_by":"auto","created_at":"2025-07-08 14:23:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1129002,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7048141/v1/e982f27b-4a6c-4c3d-b507-758e1be5d7b8.pdf"},{"id":86258240,"identity":"b41210ce-585b-485a-9dd2-b12db417d20b","added_by":"auto","created_at":"2025-07-08 14:07:24","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11527,"visible":true,"origin":"","legend":"\u003cp\u003eAppendix 1\u003c/p\u003e","description":"","filename":"Appendix11.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7048141/v1/a105f4357f1f727f55d3814c.xlsx"},{"id":86258242,"identity":"12f98381-1415-408b-99d1-7f5fedb35d7c","added_by":"auto","created_at":"2025-07-08 14:07:24","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12327,"visible":true,"origin":"","legend":"\u003cp\u003eAppendix 2\u003c/p\u003e","description":"","filename":"Appendix2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7048141/v1/c3904a028465e9ac2d02b745.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAdolescent Preferences for Vlogs and Access to Restricted Content\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn the digital era, online platforms play a central role in adolescents' daily lives, shaping both how they allocate and structure their leisure time and how they manage social relationships and shape cultural preferences (Smith, Hewitt, \u0026amp; Skrbis, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The digital space has become increasingly dominant, being recognized as a distinct social framework where users can construct their identities and interact based on shared interests (Goffman, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; McLuhan, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Meyrowitz, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1986\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In this context, YouTube stands as the most widely used content-sharing platform globally, enabling the uploading, viewing, and dissemination of a vast array of content (Figueiredo, Benevenuto, \u0026amp; Almeida, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For adolescents, subscribing to specific channels, watching, and sharing preferred content serve as key forms of leisure and socialization. Examining vlog and vlogger preferences is therefore essential for understanding the online experiences of adolescents and identifying the potential risks to which they are exposed.\u003c/p\u003e\u003cp\u003eThe YouTube platform ranks at the top of the preferences of 8th-grade students in Bihor County, as evidenced by the Omnibus 2022 research (Bihor, Oradea, \u0026amp; Bihor, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As Dynel (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) points out, communication on the YouTube platform encompasses three levels: video interaction, the interpretation of YouTube video discourse, and the interpretation of video comments. From this perspective, a person can simultaneously act as a content creator (vlogger), a content consumer (subscriber), and a content critic (with the ability to interpret received comments).\u003c/p\u003e\u003cp\u003eThis study outlines the profile of adolescents exposed to restricted content, namely those video materials categorized by the YouTube platform as unsuitable for minors. The research aims to examine Internet usage among adolescents, focusing on vlogging, and to classify respondents based on their preferred vloggers. At the same time, it is based on the premises of the uses and gratifications theory (Alan, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ruggiero, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; So, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which posits that users select content according to their preferences. While emphasizing that users choose the content they engage with, content itself plays a significant role in shaping human attitudes and behaviors, as argued by cultivation theory scholars (Morgan, Shanahan, \u0026amp; Signorielli, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Morgan, Shanahan, Signorielli, \u0026amp; Michael Morgan, 2014; Stefanone, Lackaff, \u0026amp; Rosen, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, it is highly likely that the choice of vloggers influences adolescents' attitudes and behavior.\u003c/p\u003e\u003cp\u003eThe primary aim of this study is to identify the predictors of adolescents' consumption of restricted vlog content. In terms of the paper's objectives, the focus is on analyzing the micro-communities of vloggers referenced in the MERPAS 2018 Survey and identifying the most influential vloggers, based on the preferences expressed by students. Additionally, the study seeks to determine the predictors and correlates of adolescent consumption of vloggers, including those targeting a general audience and those posting restricted content.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSince the 2000s, Romanian adolescents have rapidly integrated the internet into their daily routines. As a result, the online environment represents their primary, or even sole, means of spending their leisure time (Ştefănescu, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Compared to their peers in other European countries, Romanian adolescents have reported higher daily internet usage but significantly lower scores regarding the diversity of activities they engage in online (Balea, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mascheroni et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ragnedda \u0026amp; Muschert, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Smahel et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe prevalence of internet use among adolescents, driven by mobile phone access and challenges in parental monitoring, has made the online space key for adolescents to express their identity (Balakrishnan \u0026amp; Griffiths, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chiang \u0026amp; Hsiao, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Montes-Vozmediano, Garc\u0026iacute;a-Jim\u0026eacute;nez, \u0026amp; Menor-Sendra, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Morris \u0026amp; Anderson, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; P\u0026eacute;rez-Torres, Pastor-Ruiz, \u0026amp; Abarrou-Ben-Boubaker, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and connect with peers from offline or online life (Velicu \u0026amp; Marinescu, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this context, the online-offline dynamic and the examination of how online interactions influence the offline environment have become a focal point in scientific research (Symons, Vanwesenbeeck, Walrave, Van Ouytsel, \u0026amp; Ponnet, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Digital interactions involve constructing a self-projection and digital identity (Camacho, Minelli, \u0026amp; Grosseck, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), with varying awareness among adolescents about how online content shapes their image.\u003c/p\u003e\u003cp\u003eIn the study of vlog-type content, particular attention is given to video materials deemed inappropriate for minors (Beers F\u0026auml;gersten, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; D\u0026ouml;ring \u0026amp; Mohseni, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hattingh, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Khasawneh et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such content is classified by the YouTube community under the generic term \"restricted content\" (YouTube, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and includes the following categories: horror, hate speech, videos depicting dangerous behaviors, promoting discriminatory situations, encouraging self-harm, self-mutilation, or eating disorders (including extreme dieting), adult content (nudity and sexual content), and offensive or abusive language. Once YouTube\u0026rsquo;s algorithms, platform moderators, or users report the presence of such content in a vlog, it is classified as restricted and becomes inaccessible to certain age groups (e.g., individuals under 18 years old). For the purposes of this study, restricted vlogs are categorized as restricted online content.\u003c/p\u003e\u003cp\u003eIt is crucial to acknowledge that the online environment under discussion is governed by general norms and regulations designed to guide user behavior. In this regard, the concepts of \"deviant online behavior\" and \"restricted online content\" are central to the present study. In this context, \"deviant online behavior\" encompasses various forms of social rule violations within the digital space (Barbovschi, Green, \u0026amp; Vandoninck, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mascheroni \u0026amp; \u0026Oacute;lafsson, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Schrock \u0026amp; Boyd, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; UNICEF, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Velicu, Balea, \u0026amp; Barbovschi, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yesilada \u0026amp; Lewandowsky, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Meanwhile, restricted content refers to digital materials deemed inappropriate for specific age groups (particularly children and adolescents) due to their potentially harmful effects on behavior (Alshamrani, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Barrientos, Alaiz-Rodr\u0026iacute;guez, Gonz\u0026aacute;lez-Castro, \u0026amp; Parnell, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Boyd, Ryan, \u0026amp; Leavitt, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kaushal, Saha, Bajaj, \u0026amp; Kumaraguru, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mothe, Parikh, \u0026amp; Ramiandrisoa, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schrock \u0026amp; Boyd, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Suryawanshi, Chakravarthi, Arcan, \u0026amp; Buitelaar, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Velicu, Balea, et al., 2019). Accessing such restricted materials in the online space is considered a risky behavior in relation to Internet usage (Velicu, Balea, et al., 2019). Online risks are closely associated with problematic behaviors in the digital environment, which can adversely impact the development, value systems, and both the physical and psychological well-being of users, particularly adolescents. Concurrently, online risks are juxtaposed with online opportunities, which include content that facilitates developmental processes and socialization among users (Livingstone, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; OECD, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schrock \u0026amp; Boyd, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo understand deviant behavior and the consumption of restricted content, the theory of power and control provides an essential analytical framework. This theory emphasizes the role of factors such as gender, social status, attitude toward risk, parental supervision, and affiliation with deviant subcultures as key predictors of deviant behavior. Furthermore, the theory highlights the influence of familial cultural values, which have the capacity to shape attitudes and behaviors among younger generations (Hagan, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Hagan \u0026amp; Foster, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Hagan, Gillis, \u0026amp; Simpson, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother essential theoretical framework for analyzing restricted content is the opportunity and routine activity theory (Osgood, Johnston, Omalley, \u0026amp; Bachman, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Osgood, Wilson, Omalley, Bachman, \u0026amp; Johnston, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Vazsonyi, Javakhishvili, \u0026amp; Ksinan, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This theory focuses on the concept of unstructured socialization, defining it as the act of spending leisure time without clear direction, specific objectives, alongside peer groups, and without adult supervision (Osgood et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Unstructured socialization is considered as a potential risk for norm violation and the development of deviant behaviors, as it is associated with rewards such as alleviating boredom, immediate gratification, and integration into group hierarchies, as well as peer pressure.\u003c/p\u003e\u003cp\u003eMoreover, the opportunity and routine activity theory can also be applied to analyze deviant behaviors in the online environment. Thus, unstructured socialization in the digital space involves activities such as browsing the Internet, accessing social networks, and other similar behaviors that may contribute to the emergence of deviant acts, including cyberbullying or cyberstalking (Marcum \u0026amp; Higgins, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marcum, Higgins, \u0026amp; Ricketts, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mesch, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Navarro \u0026amp; Jasinski, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThis research focuses on Internet usage and vlogging preferences among eighth-grade students from Bihor County, based on the MERPAS Educational Survey conducted in 2018. The survey targeted eighth-grade students enrolled in mainstream education during the 2018\u0026ndash;2019 academic year, with 4333 valid responses. Of these, 2558 students reported following vlogger content, with a slightly higher proportion of males (51.6%, 1319 students).\u003c/p\u003e\n\u003cp\u003eData was collected via a Google Forms questionnaire, including a question about vlogging consumption. Students listed their most popular vloggers, and the names were standardized by replacing nicknames with official ones. A total of 997 unique vloggers were mentioned, and their channels were analyzed to identify the number of restricted videos posted on YouTube.\u003c/p\u003e\n\u003cp\u003eConnections between vloggers were established based on shared selections by respondents, allowing for network analysis in Gephi to identify key influencers and micro-communities. The study also examined the type of content accessed by adolescents by analyzing restricted videos posted by the mentioned vloggers from channel creation until November 1, 2018. This analysis, conducted in April-May 2022, covered approximately 900 channels.\u003c/p\u003e\n\u003cp\u003eFor statistical analysis, vloggers were categorized by the percentage of restricted content on their channels. A categorical variable was created to distinguish students who do not follow vlogs, those who follow general-audience vloggers, and those who follow vloggers posting restricted content. The presence of restricted content was used as an indicator of exposure to potentially inappropriate material.\u003c/p\u003e\n\u003cp\u003eIn order to achieve the aforementioned objectives (analysis of the vloggers' micro communities and identification of the most influential vloggers, as well as the analysis of predictors of vlog consumption among adolescents), sequential multinomial logistic regression was applied.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Network analysis and the modularity procedure\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe modularity function (Danon, D\u0026iacute;az-Guilera, Duch, \u0026amp; Arenas, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e) was used to identify hidden patterns by measuring the internal vs. external connections of communities, assuming that entities are more connected within groups than outside them. Network visualization was performed using the Force Atlas 2 and ForceAtlas algorithms (Bastian, Heymann, \u0026amp; Jacomy, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jacomy, Venturini, Heymann, \u0026amp; Bastian, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). which balance attraction and repulsion forces to structure the network. The Force Atlas algorithm brings less connected entities closer to highly connected ones. Model parameters included a repulsion strength of 5000.0, attraction strength of 5.0, and gravity 80 to emphasize micro-communities. The Label Adjust procedure (Bastian et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) was applied to prevent overlapping labels and improve readability.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Identifying the presence of restricted content posted by vloggers\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eIdentifying restricted content on vlog channels mentioned by students involved querying each video\u0026rsquo;s rating from the channel's creation until the MERPAS 2019 Survey. The 967 vlog channels were processed to determine posted videos, then evaluated using Google's API (Application Programming Interface) to extract YtRating, YouTube\u0026rsquo;s content restriction indicator.\u003c/p\u003e\n\u003cp\u003eA restricted content score was calculated for each channel based on the proportion of restricted videos. Each student\u0026rsquo;s score was computed by summing the percentages of restricted content from the vloggers they followed. This score was analyzed in relation to vlog preferences and the number of vloggers followed through correlation analysis.\u003c/p\u003e\n\u003cp\u003eThe variable was dichotomized due to a leptokurtic distribution. Bivariate analyses (cross-tabulations and Chi-Square tests) assessed associations between community preferences and restricted content. Students who did not follow any vlogs were included, and a new variable was created combining restricted content presence and vlog consumption.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3. Multivariate analysis of the profile of students vulnerable to watching restricted vlogs\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe profile of students vulnerable to restricted content was analyzed using sequential multinomial logistic regression. Before testing the model, assumptions and multicollinearity were assessed, with VIF values (max 1.633) and Hosmer \u0026amp; Lemeshow tests confirming validity.\u003c/p\u003e\n\u003cp\u003eSchool characteristics significantly influenced vlog preferences, as shown by the ICC values: 0.03 (restricted vs. general audience), 0.15 (restricted vs. no vlogs), and 0.06 (general audience vs. no vlogs). Due to this limitation, a sequential multinomial logistic model was estimated with five \u003cem\u003eblocks\u003c/em\u003e:\u003c/p\u003e\n\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eBlock I\u003c/em\u003e: Control variables (sociodemographic variables: gender, mother's education, parents' residential environment, and socioeconomic asset index).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eBlock II\u003c/em\u003e: Adds school performance (general grade average from the previous year).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eBlock III\u003c/em\u003e: Attitudinal variables (attitude toward risk, preference for a party lifestyle and the student's perception of parental monitoring).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eBlock IV\u003c/em\u003e: Online behavior variables, specifically the frequency of digital device use (operationalized by the factorial score of time spent on smartphones and computers) and unstructured digital socialization (operationalized using smartphones and tablets for entertainment and socialization).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eBlock V\u003c/em\u003e: Examines content type and deviant behavior (involvement in problematic school behaviors).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eIn consideration of the aforementioned factors, the study posits the following theoretical hypothesis: The probability of an adolescent engaging with vloggers targeting a general audience or those posting restricted content is influenced by their attitude toward risk, parental monitoring, school results, adoption of a hedonistic and party-oriented lifestyle, time spent online, preference for unstructured digital socialization activities, and an increase in engagement with problematic school behaviors.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4. Research hypotheses\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe theoretical hypotheses were tested through operational hypotheses based on a sequential multinomial logistic regression (in \u003cem\u003eblocks\u003c/em\u003e), in order to assess the actual effect (while controlling for the effect of other variables included in the model). The main hypotheses are:\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Hypothesis 1.\u003c/strong\u003e\u0026nbsp;The probability of following vloggers who post restricted content is higher for adolescents with a more open attitude toward risk-taking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Hypothesis 2.\u0026nbsp;\u003c/strong\u003eA lower level of parental monitoring is associated with an increased probability of following vloggers who post restricted content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Hypothesis 3.\u0026nbsp;\u003c/strong\u003e\u0026nbsp;Students with poorer academic performance are more likely to follow vloggers who post restricted content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Hypothesis 4.\u0026nbsp;\u003c/strong\u003eThe adoption of a lifestyle focused on entertainment and a party-oriented leisure activity increases the likelihood of belonging to the group of adolescents who follow restricted-content vlogs, compared to the group of students who do not follow vloggers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Hypothesis 5.\u0026nbsp;\u003c/strong\u003eThe more time adolescents spend online, the higher the likelihood that they will follow vloggers who post restricted content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Hypothesis 6.\u003c/strong\u003e\u0026nbsp;The preference for unstructured digital socialization, operationalized through the extent of use of electronic devices for socializing and entertainment, increases the chance of following vloggers who post restricted content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026bull; Hypothesis 7.\u003c/strong\u003e\u0026nbsp;Engaging in problematic school behaviors increases the chance of belonging to the category of students who watch content-restricted vlogs.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results and discussions","content":"\u003cp\u003eBased on the theoretical model presented, which investigates exposure to restricted content from the perspective of classical approaches to the study of deviance (theories of power and control, opportunity and routine activities theory), a model was estimated to assess the influence of predictors identified in the relevant literature on a specific adolescent behavior, namely, following vloggers who post restricted content.\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1. Preliminary analyses. Network of students following vloggers\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eA bipartite network of 3,157 nodes (entities) and 5,406 edges (connections) was constructed to map students\u0026apos; vlog preferences. The analysis reveals an average of three connections per entity, a modularity score of 0.642, and 115 identified communities, most of them sparsely connected. The largest communities (named after their main hub) include Selly Class (20.65%), Hungarian Vloggers Class (8.96%), Codrin Bradea Class (8.24%), and MaxInfinite Class (7.51%), among others.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2. Distribution and Analysis of Restricted Content\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eGiven the high prevalence of restricted content (over 10 vlogs age-restricted before September 2018), this type of content is mainly found among vloggers with few mentions, except for Codrin Bradea (166 mentions), PewDiePie (118), and ZappyTV (32). Most of these vloggers belong to the modularity classes of PewDiePie (42), Codrin Bradea (58), and Hungarian Vloggers (43).\u003c/p\u003e\n \u003cp\u003eThe Summative Score for Restricted Content was calculated by aggregating the content ratings of watched channels. Results indicate that students primarily follow vloggers with minimal restricted content, though some also follow those frequently sanctioned, likely due to language use.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics for the Summative Restricted Content Score\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSummative Score of Restricted Content\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE (Standard Error)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMedian\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVariance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStandard deviation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMinimum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMaximum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSkewness\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eKurtosis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e presents the descriptive statistics for the Summative Restricted Content Score. Given that 72% of valid channels (586 out of 807) had no restricted videos during the analyzed period, a dichotomous variable was created to distinguish channels with at least one restricted video from those targeting a general audience.\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBivariate analysis of the Summative Restricted Content Score\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of vloggers followed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModularity Classes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eModularity Classes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003cp\u003e(Spearman)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSummative Restricted Content Score\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.205\u003c/strong\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(Pearson Correlation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.161\u003c/strong\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(Spearman)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eBivariate analysis (\u003cstrong\u003eTable 2\u003c/strong\u003e) shows a significant correlation between the Summative Restricted Content Score and modularity classes (Pearson: 0.205***, Spearman: 0.161**). The Chi-Square test (\u003cstrong\u003eTable 3\u003c/strong\u003e) confirms a statistically significant association between modularity classes and restricted content (\u0026chi;\u0026sup2; = 177.631, df = 19, p \u0026lt; 0.0001). Preferences for certain communities (Codrin Bradea, PewDiePie, Vlad Munteanu, Selly, Logan Paul) are linked to restricted content, while others (Andreea Balaban, ACE Family, Andra Gogan, GajuKyd, Hungarian vloggers) show a lower likelihood of such content.\u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation between the presence of restricted content and modularity classes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAsymptotic Significance\u003c/p\u003e\n \u003cp\u003e(2-sided)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePearson Chi-Square\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177.631\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLikelihood Ratio\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLinear-by-Linear Association\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN of Valid Cases\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3. Analysis of the profile of users engaging with restricted content\u003c/h2\u003e\n \u003cp\u003eNetwork analysis indicates that students who watch restricted content also follow general-audience vloggers, forming micro-communities. Vloggers with at least one restricted video appear in all modularity classes with at least four entities. While most vloggers have not posted restricted content, all communities include at least one who has, amplifying their influence.\u003c/p\u003e\n \u003cp\u003eThe summative score for restricted content correlates weakly but directly with modularity classes and the number of vloggers followed, suggesting that students who follow more vloggers are more likely to engage with restricted content. The Chi-Square test confirms this association, highlighting modularity classes with a higher presence of restricted content (Codrin Bradea, PewDiePie, Vlad Munteanu, Selly, Logan Paul) and those linked to general-audience content (Andreea Balaban, ACE Family, Andra Gogan, Hungarian vloggers, Gajukyd, MaxInfinite).\u003c/p\u003e\n \u003cp\u003eGiven adolescents\u0026rsquo; varied exposure to restricted content, further analyses were conducted to profile those most vulnerable, considering sociodemographic factors. Since the dependent variable is categorical (with three categories), sequential multinomial logistic regression (in \u003cem\u003eblocks\u003c/em\u003e) was used to compare the impact of independent variables on the categories of the dependent variable, relative to the reference category.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted logit (Y1)\u0026thinsp;=\u0026thinsp;Intercept (Constant) + \u0026beta;1\u0026thinsp;+\u0026thinsp;\u0026beta;2\u0026thinsp;+\u0026thinsp;\u0026beta;3\u0026thinsp;+\u0026thinsp;\u0026beta;..+ e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003ePredicted logit\u003c/em\u003e (students who follow general audience vloggers vs. students who follow vloggers with restricted content): Significant predictors include sex, mother\u0026rsquo;s education, academic results, attitude toward risk, and unstructured digital socialization.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003ePredicted logit\u003c/em\u003e (students who do not follow any vlog vs. students who follow vloggers with restricted content): Key predictors are sex, mother\u0026rsquo;s education, academic results, attitude toward risk, and unstructured digital socialization.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAt first glance, the estimated models demonstrate an improvement in fit compared to the previous model, with the greatest contribution to predicting the likelihood of belonging to the reference category observed in \u003cem\u003eBlock III\u003c/em\u003e. This \u003cem\u003eblock\u003c/em\u003e includes predictors such as attitude toward risk, leisure oriented toward entertainment and parties, and parental monitoring. These factors play a significant role in explaining the likelihood of belonging to the reference category of students following vloggers with restricted content.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Statistics Regarding Model Fit\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003eBlock\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u0026nbsp;Fitting Criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Likelihood Ratio Tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2 Log Likelihood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSquare\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBlock 1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eIntercept\u0026nbsp;Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e627.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e638.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e623.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eFinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e542.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e596.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e522.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e101.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBlock 2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eIntercept\u0026nbsp;Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2350.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2360.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2346.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eFinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2248.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2313.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2224.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e121.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBlock 3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eIntercept\u0026nbsp;Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3515.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3526.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3511.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eFinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3358.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3455.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3322.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e189.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBlock 4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eIntercept\u0026nbsp;Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3515.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3526.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3511.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eFinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3329.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3448.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3285.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e225.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBlock 5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eIntercept\u0026nbsp;Only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3351.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3362.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3347.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eFinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3166.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3295.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3118.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e229.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistics Regarding Goodness of Fit\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGoodness of Fit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChi-Square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eBlock 1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePearson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e186.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eBlock 2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePearson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1697.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1787.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eBlock 3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePearson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3313.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3322.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eBlock 4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePearson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3303.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3285.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eBlock 5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePearson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3166.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3118.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePseudo R-Square Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePseudo R-Square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBlock 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBlock 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBlock 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBlock 4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBlock 5\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCox and Snell\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNagelkerke\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMcFadden\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe final model provides a better explanation of the probability of belonging to a specific category of students (those following vloggers posting restricted content) compared to the baseline model with only the constant. Model fit statistics show significant improvements (Chi-Square\u0026thinsp;=\u0026thinsp;229.405***, df\u0026thinsp;=\u0026thinsp;22), with Pseudo R-Square values indicating a better model fit in \u003cem\u003eBlock 5\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eAs demonstrated in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the model fit statistics show improvements across the \u003cem\u003eblocks\u003c/em\u003e, with significant differences in Chi-Square values (p\u0026thinsp;\u0026lt;\u0026thinsp;.000) as the number of predictors increases. In Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the goodness-of-fit statistics show a Pearson Chi-Square value of 3166.235 (df\u0026thinsp;=\u0026thinsp;3144, p\u0026thinsp;=\u0026thinsp;0.387), indicating an adequate model fit, while Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the Pseudo R-Square values, which demonstrate an improvement from \u003cem\u003eBlock 1\u003c/em\u003e to \u003cem\u003eBlock 5\u003c/em\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4. Analysis of the Impact of Key Predictors\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn the present analysis, the significant results are supported by data from Tables \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e (see \u003cem\u003eAppendix 1 and 2\u003c/em\u003e), which illustrate key predictors of adolescent vlog content consumption, including parental education, lifestyle, risk attitude, and digital socialization (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e also highlights the correlation between academic performance and the type of content followed.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eThe final model identifies key predictors of following vloggers who post restricted content, including gender, maternal education, academic achievement, attitude toward risk, party lifestyle, unstructured digital socialization, and frequency of device use for internet access. Significant predictors include unstructured digital socialization (Chi-Square\u0026thinsp;=\u0026thinsp;31.981, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), maternal education (Chi-Square\u0026thinsp;=\u0026thinsp;25.425, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), risk attitude (Chi-Square\u0026thinsp;=\u0026thinsp;22.360, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and academic achievement (Chi-Square\u0026thinsp;=\u0026thinsp;18.147, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Gender and party lifestyle also contribute significantly.\u003c/p\u003e\n \u003cp\u003eBoys are more likely to follow restricted content vloggers, while girls prefer general content (B = -0.469, Exp(B)\u0026thinsp;=\u0026thinsp;0.625, p\u0026thinsp;=\u0026thinsp;0.002). Students whose mothers completed at least lower secondary education are more likely to follow restricted content vloggers (B\u0026thinsp;=\u0026thinsp;0.825, Exp(B)\u0026thinsp;=\u0026thinsp;2.282, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). A pro-risk attitude also increases the likelihood of following restricted content vloggers (B = -0.348, Exp(B)\u0026thinsp;=\u0026thinsp;0.706, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). However, no significant difference in risk attitude was found between those following restricted versus general content vloggers.\u003c/p\u003e\n \u003cp\u003eRegarding the formulated hypotheses, the \u003cstrong\u003efirst hypothesis\u003c/strong\u003e suggests that a pro-risk attitude increases the likelihood of exposure to restricted content among adolescents, supported by previous studies (Khurana et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nesi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Valkovičov\u0026aacute;, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, no significant difference was found between the likelihood of following general-audience vs. restricted content vloggers. This aligns with Haridakis and Hanson (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), who note that a risk-taking attitude predicts YouTube content consumption, but the effect diminishes when content-watching motives are controlled. Both groups of followers, general-audience and restricted content, share a pro-risk attitude.\u003c/p\u003e\n \u003cp\u003eThe \u003cstrong\u003esecond hypothesis\u003c/strong\u003e examines the role of parental monitoring in predicting the following of vloggers with restricted content. The results do not support this hypothesis, as no evidence suggests that students with less parental monitoring are more likely to access inappropriate vlogs. This contrasts with findings in the literature on problematic internet use and online victimization (Brighi, Menin, Skrzypiec, \u0026amp; Guarini, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). A meta-analysis on the relationship between social media use and parent-child relationships found weak negative correlations between both general upbringing and specific parental control, including monitoring (Lukavsk\u0026aacute;, Hrabec, Lukavsk\u0026yacute;, Demetrovics, \u0026amp; Kiraly, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eParental monitoring does moderate the relationship between restricted content exposure and alcohol consumption, though its impact is weak and lacks long-term effects (Smout et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Online mediation styles also influence social media use (Beyens, Keijsers, \u0026amp; Coyne, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Geurts, Koning, Vossen, \u0026amp; van den Eijnden, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Page Jeffery, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Research shows that instructive parental mediation reduces the risk of online victimization (Rega, Gioia, \u0026amp; Boursier, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wachs, Costello, et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), with gender differences observed in parental monitoring (Wallace, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, girls are asked more frequently about their social media activity than boys, providing support for the hypothesis of gender differences in parenting and behavior, as posited by control and power theorists (Hadjar, Baier, Boehnke, \u0026amp; Hagan, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). The inconsistency of findings regarding the impact of parental monitoring in relation to exposure to restricted content, as documented in the literature, aligns with the results obtained.\u003c/p\u003e\n \u003cp\u003eThe \u003cstrong\u003ethird hypothesis\u003c/strong\u003e, regarding the negative association between academic performance and following vloggers with restricted content, is rejected. Academic performance is positively associated with following restricted content vloggers (B = -0.224, Exp(B)\u0026thinsp;=\u0026thinsp;0.799, p\u0026thinsp;=\u0026thinsp;0.001), contrary to expectations. This suggests that better academic performance may lead to greater permissiveness regarding internet use, particularly for boys. Despite parental concerns about the impact of social media on school performance (Velicu, Chaudron, Dias, Brito, \u0026amp; Lobe, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), literature shows insufficient evidence to support this hypothesis (Astatke, Weng, \u0026amp; Chen, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marker, Gnambs, \u0026amp; Appel, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sampasa-Kanyinga, Chaput, \u0026amp; Hamilton, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, restricted content vloggers often share gaming content in English, suggesting that English proficiency may explain the academic performance differences between students who follow general vs. restricted content vloggers.\u003c/p\u003e\n \u003cp\u003eThe \u003cstrong\u003efourth hypothesis\u003c/strong\u003e, which posits a link between a party-oriented lifestyle and the likelihood of following vloggers with restricted content, is supported. The final model shows that students with a disposition towards a party lifestyle are more likely to follow restricted content vloggers compared to those who do not follow any vlog channels (B = -0.174, Exp(B)\u0026thinsp;=\u0026thinsp;0.840, p\u0026thinsp;=\u0026thinsp;0.033). However, no significant difference was found between followers of restricted and general content vloggers in terms of party lifestyle (B\u0026thinsp;=\u0026thinsp;0.131, Exp(B)\u0026thinsp;=\u0026thinsp;1.139, p\u0026thinsp;\u0026gt;\u0026thinsp;0.1).\u003c/p\u003e\n \u003cp\u003eThe findings are consistent with the literature, which reveals that a party-oriented lifestyle and the posting of photos and videos with individuals enjoying parties are valued by adolescents who participate in club and nightclub outings (Kavanaugh \u0026amp; Anderson, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pav\u0026oacute;n-Ben\u0026iacute;tez, Romo-Avil\u0026eacute;s, \u0026amp; S\u0026aacute;nchez-Gonz\u0026aacute;lez, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The adoption of a party-oriented lifestyle is influenced by dispositional factors, affected by social status (Calderon Gomez, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fornari, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hatos, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Murdock, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; North, Snyder, \u0026amp; Bulfin, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Robinson, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sheldon, Antony, \u0026amp; Sykes, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Webster, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yates, Kirby, \u0026amp; Lockley, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), and active participation in parties and other social activities is a risk factor related to intensive social media use (Sheldon et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). At the same time, the presence of social and dispositional factors influencing the use of the YouTube platform has been documented in the literature (Balakrishnan \u0026amp; Griffiths, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Haridakis \u0026amp; Hanson, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Taylor \u0026amp; Cingel, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, no differences were found in the preference for a party-oriented lifestyle between followers of general and restricted content vlogs. These findings support the opportunity and routine activity theory, as well as the control and power theory, linking subcultural preferences with behavior (Hagan, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e; Hatos, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eStatistically significant differences were found between students who follow restricted vlogs and those who do not, with the former using the Internet more frequently (B = -0.126, Exp(B)\u0026thinsp;=\u0026thinsp;0.882, p\u0026thinsp;=\u0026thinsp;0.059). No significant differences were observed between those who follow restricted and general audience vlogs (B = -0.048, Exp(B)\u0026thinsp;=\u0026thinsp;0.953, p\u0026thinsp;\u0026gt;\u0026thinsp;0.5). These findings partially confirm the \u003cstrong\u003efifth hypothesis\u003c/strong\u003e, that increased online time is linked to a higher likelihood of following restricted content, as supported by previous research (Laconi et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, time spent online leads to a proportional increase in exposure to restricted content (Livingstone, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Livingstone, \u0026Oacute;lafsson, et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jan Van Dijk \u0026amp; Hacker, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Velicu, Balea, et al., 2019; Velicu \u0026amp; Marinescu, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wachs, Mazzone, et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Time spent online is also linked to greater exposure to both risks and opportunities in the digital environment (Bedrosova, Machackova, \u0026Scaron;erek, Smahel, \u0026amp; Blaya, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Demeter, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Diaconescu, Barbovschi, \u0026amp; Baciu, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Livingstone, Mascheroni, \u0026amp; Stoilova, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mascheroni \u0026amp; \u0026Oacute;lafsson, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Oksanen, Hawdon, Holkeri, Nasi, \u0026amp; Rasanen, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Velicu, Balea, et al., 2019) and mediates the relationship between problematic use and parental monitoring (Brighi et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The time spent online does not significantly differentiate between following general vs. restricted vlogs. However, differences are found between boys from wealthier families who follow restricted vlogs and girls from lower socioeconomic status families who do not follow any vlog channel, suggesting a digital divide (DiMaggio \u0026amp; Garip, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; DiMaggio \u0026amp; Hargittai, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; DiMaggio, Hargittai, Celeste, \u0026amp; Shafer, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; Hatos, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Deursen \u0026amp; Van Dijk, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jan Van Dijk, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe probability of following vloggers who post restricted content is directly related to phone/tablet use for entertainment and socializing. Students who follow restricted content vloggers use their phones more often for social media and entertainment compared to those who follow general audience vloggers (B = -0.208, Exp (B)\u0026thinsp;=\u0026thinsp;0.813, p\u0026thinsp;=\u0026thinsp;0.014) and those who do not follow any vlog (B = -0.394, Exp (B)\u0026thinsp;=\u0026thinsp;0.675, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These findings support the \u003cstrong\u003esixth hypothesis\u003c/strong\u003e, which tests the relationship between the type of online activities and the degree of exposure to inappropriate content for minors. The results are consistent with studies in the field (Laconi et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Livingstone, Davidson, Batool, Ciaran, \u0026amp; Anulekha, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mascheroni \u0026amp; \u0026Oacute;lafsson, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sanders et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Smout et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Adolescents who primarily use smartphones for socializing and entertainment are more vulnerable to exposure to restricted content on YouTube. Students spending more time on social media and entertainment are more likely to follow vloggers who post restricted content, confirming the familiarity effect and the bond formed through informal language (Beers F\u0026auml;gersten, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe existence of mechanisms that create attachment to specific vloggers and a sense of belonging to a community of users sharing similar interests is akin to the adoption of deviant subcultures, as documented by Hagan (\u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e). From the perspective of opportunity and routine activity theory, unstructured digital socialization is one of the main predictors of online deviance (Marcum et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Navarro \u0026amp; Jasinski, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Reyns, Henson, \u0026amp; Fisher, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wachs, Costello, et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wachs, Mazzone, et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wolfe, Marcum, Higgins, \u0026amp; Ricketts, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), and constitutes as an equivalent to unstructured socialization. Social media platforms represent a space where adolescents can plan activities in the absence of a responsible guardian (Meldrum \u0026amp; Clark, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pyrooz, Decker, \u0026amp; Moule, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe \u003cstrong\u003eseventh hypothesis\u003c/strong\u003e suggests a positive relationship between following restricted content on YouTube and engaging in problematic school behaviors, but the results do not support this. Students who access restricted vlogs do not differ significantly from those who follow general audience vlogs or no vlogs in terms of problematic school behaviors. The interpretation of the results regarding the profile of students who follow restricted vlogs indicates similarities with the profile of adolescents who violate copyright laws, as studied by Hagan and Kay (\u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e) from the perspective of power and control theory. These students are typically boys from higher social-status families, perform well academically, have access to Internet-connected devices, display a pro-risk-taking attitude, and identify with youth culture. Watching restricted vlogs also serves as a form of socialization, teaching workplace behaviors (e.g., gaming, foul language, irony) and connecting adolescents from wealthy families with future labor market positions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study presents the following of restricted vlogs as a specific form of exposure to restricted online content, characteristic of adolescents from higher social-status families, and particularly popular among boys. Classified within the category of online risks (risky online content), the consumption of potentially restricted online content is predicted by attitudes toward risk, a lifestyle inclined toward parties and entertainment, time spent online, and gender. According to the results obtained, girls with a pro-risk attitude, who show openness toward attending parties and spend more time online, perceive themselves as more exposed to potentially restricted content. Furthermore, the findings indicate that students consider other virtual environments where this content is accessed to be more dangerous than social networks and entertainment content. In this regard, the negative association between the use of electronic devices for entertainment/socialization and exposure to potentially restricted content is particularly illustrative.\u003c/p\u003e\u003cp\u003eConsidering that a party-oriented lifestyle (leisure inclined toward fun and partying) serves as a proxy for unstructured offline socialization, and the use of electronic devices for entertainment/socialization serves as a proxy for unstructured virtual socialization, the result can be interpreted as evidence of the stronger influence of unstructured socialization in offline life, even compared to activities conducted in the virtual environment. Consequently, offline life aspects exert a significant influence on adolescent behavior, including online risk behaviors (exposure to potentially restricted content) and deviant behaviors (violent online interpersonal deviance). The results also suggest the potential presence of compositional effects related to the pro-risk attitude and the adoption of a party-oriented lifestyle. Thus, the incidence of violent online interpersonal deviance and exposure to potentially restricted content shows an increase in schools with a negative school climate and a higher proportion of boys from lower social-status families.\u003c/p\u003e\u003cp\u003eA distinct category of restricted content includes images, video sequences, and audio materials classified as inappropriate for minors. This category includes horror content, violent material, sexually explicit content, materials encouraging dangerous behaviors, hate speech, and offensive language. By analyzing the preference for following vlogs labeled by YouTube as inappropriate for minors (restricted vlogs), a profile emerges of adolescents interested in this content, providing a clearer understanding of the phenomenon studied. Thus, boys from educated families, with a pro-risk attitude, and openness to adopting a party-oriented lifestyle, spend considerable time on social media. For these adolescents, digital socialization is more important than offline socialization, and the digital environment is an integral part of their lives and a way of expressing their identity. Accessing restricted content serves as a substitute for engaging in deviant offline behaviors and provides a way to familiarize themselves with behaviors and attitudes valued in the professional environment they will later join. In this regard, a significant portion of the identified restricted vlogs contain content such as gaming, political satire, and stand-up comedy. The presence of significant differences at the school unit level between students who follow restricted content and those who do not follow any vloggers signals the existence of disparities in terms of Internet access and/or compositional effects, which deepen the divide between students who regularly use the Internet and those with occasional access to digital technology.\u003c/p\u003e\u003cp\u003eAlthough the necessary assumptions for the analyses were strictly verified, it is important to consider the various \u003cb\u003elimitations\u003c/b\u003e of the study when interpreting the results. Firstly, the presence of a high number of missing values that are not random may influence the results obtained. As such, the multinomial regression models account for only 1,657 cases out of a total of 4,333 valid cases.\u003c/p\u003e\u003cp\u003eRegarding the measurement of the variables included in the study, it is important to note that these are self-reports, meaning activities reported by the participating students. In this context, the study includes measurements of perceptions regarding various behaviors, which may be influenced by factors such as social desirability.\u003c/p\u003e\u003cp\u003eThe evaluation of the presence of restricted content is another aspect that raises questions regarding validity. For example, querying the number of restricted videos on a channel was conducted approximately three years after data collection, which may influence the identification of certain channels/restricted videos (during that period, it is possible that some channels or videos may have been removed by the creators). It is also possible that the presence of restricted content was reported after the completion of the survey. Consequently, some content currently evaluated as restricted may have been considered general audience content during the period when students watched it. The consistency between the restrictions applied to international channels (English vlogs) and Romanian channels is another unknown factor.\u003c/p\u003e\u003cp\u003eFurthermore, regarding restricted content, the use of dichotomous variables may influence the results obtained. Since dichotomous variables (which underpinned the categorical variable for restricted content presence) do not allow for the identification of the degree of exposure to deviant content, this study does not differentiate between students who watch vloggers who occasionally/accidentally post restricted content and those who habitually post such videos. The decision to use this type of variable was based on the leptokurtic distribution of the dependent variable, percentage of vloggers who post restricted content, where the values of 0 predominated. The differentiation between students who do not watch any vloggers and those who watch general audience vlogs constitutes another supporting factor for the decision to use the categorical variable for restricted content presence. To ensure an interpretation of the results obtained, multinomial logistic regressions were estimated, where each of the three categories of the dependent variable served as a reference category.\u003c/p\u003e\u003cp\u003eAnother limitation of this study concerns the dynamic nature of vlog preferences. Analyzing data from 2018\u0026ndash;2019 regarding the vlog preferences of eighth-grade students may be questioned in terms of reflecting current vlog preferences of adolescents, given the volatility with which the rankings of content creators' vlogs change.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlan MR (2009) In: M. RL, Nabi BO (eds) Uses and Gratifications An Evolving Perspective of Media Effects. 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Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.youtube.com/howyoutubeworks/policies/community-guidelines/\u003c/span\u003e\u003cspan address=\"https://www.youtube.com/howyoutubeworks/policies/community-guidelines/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Oradea","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"vlogs, adolescents, online preferences, restricted content, online risk","lastPublishedDoi":"10.21203/rs.3.rs-7048141/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7048141/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe digital space provides a distinct environment in which today's adolescents can engage in various activities, including watching diverse content and interacting with friends. However, challenges arise when the materials or content accessed are not age-appropriate. Watching vlogs has become a particularly significant activity in adolescents' lives. This article highlights the preferences of Romanian adolescents for vloggers and their exposure to restricted content on the YouTube platform. The present study is based on a sample of 2,558 eighth-grade students from Bihor County, Romania, who participated in a survey conducted in 2018. The methodological approach combines social network analysis to identify vlogger micro-communities and sequential multinomial logistic regression to assess the impact of sociodemographic factors, attitudes, and behaviors on the consumption of restricted content. The findings indicate that adolescents tend to favor vloggers who post restricted content, with significant correlations to variables such as gender, maternal education level, risk attitudes, and engagement in unstructured digital socialization. Vloggers from popular communities are more frequently associated with restricted content, and students with higher academic performance exhibit a greater likelihood of accessing such content.\u003c/p\u003e","manuscriptTitle":"Adolescent Preferences for Vlogs and Access to Restricted Content","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 14:07:20","doi":"10.21203/rs.3.rs-7048141/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33816824-87be-47e0-9212-9290e67c7023","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51064246,"name":"Sociology"}],"tags":[],"updatedAt":"2025-07-08T14:07:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 14:07:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7048141","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7048141","identity":"rs-7048141","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}