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Abstract

While cyberbullying has been recognized as a critically important social problem, a void remains regarding the role of digital media. To address this gap, we propose the social-ecological model of cyberbullying, an expanded model that builds on Bronfenbrenner’s ecological systems theory (EST) and expands Swearer and Espelage’s social-ecological model of bullying. A strength of the proposed model is the addition of the digital context as a new ecology in the everyday lives of youth, which is closely interconnected with all the other systems. Furthermore, the model incorporates digital-specific factors within each ecological system of the original EST model. This provides scholars with a holistic model that they can test, finetune, and expand. A practical implication of the model is that it can guide the creation and implementation of effective and age-appropriate cyberbullying prevention and intervention approaches because it considers in the chronosystem life phases, life transitions, historical events, and crises.

Keywords

Cyberbullying digital context digital media social-ecological model of cyberbullying theoretical model

Introduction

Parents, educators, and experts have described cyberbullying as an increasingly prevalent social problem in the life of youth (PACER, 2022). Cyberbullying includes a range of harmful behaviors such as sending, posting, or sharing negative, harmful, or false content about a person and is facilitated through digital media. Digital media are defined as the devices (e.g. cellphones and laptops) and applications (e.g. Snapchat and Instagram) used to access, produce, consume, and exchange information in digital form for supporting peer interactions, gaming, and self-presentation (Quan-Haase et al., 2018). In Canada, one-third of youth aged 15–29 years reported having been victims of cyberbullying (Statistics Canada, 2016), and the Pew Research Center reported that in the United States, the prevalence was almost double (Anderson, 2018). Even more concerning is that the prevalence of cyberbullying increased dramatically during the COVID-19 pandemic (Kee et al., 2022) because youth spent more time online (Gordon, 2020). These statistics cannot be ignored as victimization can have serious and irreversible consequences for youth including increased anxiety, higher levels of depression, and suicide ideation (Patchin and Hinduja, 2020). With youth spending increased amounts of time online in a post-pandemic era (Perrin and Atske, 2021), understanding cyberbullying in this life stage becomes a pressing social issue.

An extensive body of knowledge has accumulated after decades of research on offline bullying (Hymel and Swearer, 2015). This body of knowledge includes Swearer and Espelage’s (2004) influential social-ecological model, which identifies important factors affecting bullying within different social contexts. The model has provided a systematic understanding of bullying perpetration and victimization and has informed prevention and intervention initiatives (Hong et al., 2019). For some scholars cyberbullying is a form of bullying (Kowalski et al., 2014), but for others it represents a distinct phenomenon. For Giumetti and Kowalski (2016) they share some overlap, yet they do not see cyberbullying as simply an extension of offline bullying. Similarly, for Peebles (2014), as novel digital media emerge, cyberbullying continues to evolve further distinguishing it from offline bullying. This makes the study of cyberbullying in youth challenging, as existing theories of offline bullying can provide a roadmap but cannot simply be applied to this novel phenomenon. This makes it necessary to develop concepts and theories that address cyberbullying specifically (Sheanoda et al., 2021).

A key problem is that bullying theories do not sufficiently take into consideration the digital context in which cyberbullying takes place (McMahon, 2014). Researchers such as Hinduja and Patchin (2018) have started studying the digital context, identifying unique characteristics that impact cyberbullying such as anonymity, the perception that online spaces are free of rules, and the disinhibition effect, which describes how individuals feel a lack of restraint when online. Despite the relevance of this initial research, there continues to be a lack of systematic and coherent integration of cyberbullying-specific findings across various social contexts (Tanrikulu, 2015). This necessitates the development of a theoretical model that brings existing research on cyberbullying together with the aim of consolidation.

The aim of this article is to develop a social-ecological model of cyberbullying focused on youth by building on Bronfenbrenner’s (1979) ecological systems theory (EST) and Swearer and Espelage’s (2004) social-ecological model of bullying. Scholars have applied EST to cyberbullying research (e.g. Wright, 2016), but this work has been rather limited in scope because it has focused primarily on two areas of the larger model: the individual (e.g. age, socioeconomic status [SES], gender)—where individuals are placed at the center of their development and affected by their surroundings—and the microsystem—the social groups that individuals interact with, such as parents, friends/peers, and educators. Despite EST’s potential utility to inform cyberbullying scholarship, it has not yet been systematically applied. A key strength of the proposed expanded social-ecological model of cyberbullying is that it applies EST to integrate, compare, and organize the cyberbullying literature focused on youth in a coherent way to incorporate digital-specific factors within each ecological system. A second strength is our recognition of the digital context as a separate ecological system yet one that is closely interconnected with the other ecological systems. Furthermore, the expanded model incorporates digital-specific factors within each ecological system and explains how they interact with cyberbullying. A practical application of this holistic model is that it can guide the creation and implementation of cyberbullying prevention and intervention initiatives tailored to youth. This is a critical outcome because studies have continuously demonstrated a need for more effective and age-appropriate cyberbullying-focused initiatives (Faucher et al., 2020; Jackson et al., 2019).

The article begins with an overview of prior models and theories, examining how these have evolved and their applicability to cyberbullying-related research. The article then proposes the social-ecological model of cyberbullying as an expanded model that systematically addresses the role of digital media in cyberbullying. While cyberbullying affects individuals across the lifespan and in diverse social contexts such as workplaces, political arenas, and leisure (Myers and Cowie, 2019), the current research focus centers on youth, specifically children and adolescents under the age of 19 (United Nations, 2020), because they are most vulnerable to and affected by cyberbullying (Anderson, 2018; Patchin and Hinduja, 2020). The article concludes by discussing the strengths of the social-ecological model of cyberbullying and opportunities for testing, finetuning, and expanding the model.

Background

To provide the necessary context to develop the expanded model of cyberbullying, we provide a brief overview of Bronfenbrenner’s (1979) EST by discussing how it has evolved and highlighting key contributions (Figure 1). A strength of EST lies in its explanation of how an individual interacts and develops throughout the life course within varying social contexts (Corsaro, 1985). Most importantly, these contexts are not seen as silos, but rather the model suggests they overlap and interact with one another. EST refers to social contexts as ecological systems and sees these as places where individuals influence and are influenced by social relations and the broader culture (Swearer and Espelage, 2004).

Figure 1.

Bronfenbrenner’s ecological systems theory.

EST has continued to evolve. During his career, Bronfenbrenner revisited the model, making several important modifications (Eriksson et al., 2018). For instance, in the 1980s to mid-1990s, significant changes to the model included emphasizing close, reciprocal face-to-face interactions within a child’s immediate environment (Bronfenbrenner and Ceci, 1994) and taking more fully into consideration the development of the chronosystem, accounting for changes over time, and how these affect an individual’s developmental outcomes (Bronfenbrenner, 1986). From the mid-1990s to 2006, Bronfenbrenner (1995) developed concepts such as proximal processes, which constitute reciprocal interactions with other individuals, objects, and symbols that take place over time. Since its development, many studies have drawn on Bronfenbrenner’s original EST and later developments to study a wide range of research questions.

As digital technologies weave into many aspects of everyday life, Johnson and Puplampu (2008) took an EST approach when examining the influence of Internet use on child development. The authors propose a techno-subsystem that is integrated into the EST model as an extension of the microsystem. For Johnson and Puplampu (2008), this techno-subsystem includes an individual’s interaction with their microsystem relationships as well as material components of communication, information, and recreation technologies. The limitation of their approach, as discussed by Navarro and Tudge (2022), is that they conceptualize the techno-subsystem as intrinsically tied to the microsystem. Thus, the techno-subsystem cannot be expanded to other ecological systems, providing solutions only at the microsystem level. To study online behaviors like cyberbullying, a more comprehensive model is needed.

The social-ecological model of bullying

Building on Bronfenbrenner’s EST, Swearer and Espelage (2004) developed the social-ecological model of bullying (Figure 2). In this model, bullying results from a dynamic interplay between individuals and their social contexts. Key characteristics found to impact youth are the type and amount of social support available in and out of school, the school’s use of community partnerships and resources (Leff et al., 2004), and interactions taking place between parents and educators (Swearer and Hymel, 2015). Taken together, these factors highlight the interplay between an individual’s various ecological systems, where congruency between them helps to mediate the occurrence and impacts of bullying behaviors (Swearer and Espelage, 2004).

Figure 2.

Swearer and Espelage social-ecological model of bullying.

The social-ecological model has advanced understandings of offline bullying because of its flexibility, allowing scholars to apply it to suit the needs of their research questions and methodological approaches. For example, Hong et al. (2019) required flexibility to assess the structure of bully and victim social groups in South Korea. The authors examined the presence and absence of several relationship variables (e.g. socio-demographic variables, quality of peer relationships, school activities) within different social-ecological systems (e.g. family, friends/peers, school). While some findings paralleled existing research (Maunder and Crafter, 2018), Hong et al. (2019) provided deeper insight into which variables, and in what system, help to explain bullying while considering variance in other relevant variables.

A key benefit of the social-ecological approach is its positive impact on prevention and intervention approaches because it addresses different ecological systems and targets multiple risk factors (Bradshaw, 2015). For instance, unlike other models, using the social-ecological model can help to organize comprehensive programs for bullying prevention (Espelage and Swearer, 2010) and develop targeted interventions at the level of individuals, teachers, schools, and communities (Hong et al., 2019).

The social-ecological model of cyberbullying

Recognizing the value of the social-ecological model of bullying, scholars have started applying EST to studies of cyberbullying. These studies have primarily focused on the individual level and microsystem, with few studies also addressing the mesosystem (Wright, 2016). Despite the potential of EST for informing research, it has not been systematically applied. This is because cyberbullying is a complex phenomenon and situating digital-specific factors within all the ecological systems is difficult. To fill this gap in the literature, the present article builds on Bronfenbrenner’s EST to examine the role of the digital context in cyberbullying to provide scholars with a multi-layered explanation. The proposed model in this article addresses not only the long-standing “blind spot” of disregarding the digital context (McMahon, 2014), but it also helps to uncover the complex interactions among various factors and contexts that contribute to cyberbullying (Wright, 2016). The expanded model has a focus on youth, specifically children and adolescents under the age of 19 (United Nations, 2020). This age group has been identified as vulnerable to and affected by cyberbullying (Anderson, 2018; Patchin and Hinduja, 2020), with studies showing a need for age-appropriate cyberbullying-focused initiatives (Faucher et al., 2020; Jackson et al., 2019).

Individual level

Many factors at the individual level contribute to cyberbullying perpetration and victimization including a person’s gender, race/ethnicity, sexual orientation, religion, and presence or absence of disability (Zhu et al., 2021). Many of these factors intersect with digital-specific factors. At the individual level (Figure 3), digital-specific factors of relevance are an individual’s access to and use of digital media because these factors increase a youth’s availability and social accessibility, which increases potential cyberbullying perpetration and victimization (Englander, 2019). While not all youth have the same degree of access (Livingstone et al., 2021), even without regular access, youth can fall victims to cyberbullying (Rossow, 2018) and experience the repercussions (e.g. offline consequences to online victimization) (Ferrara et al., 2018). In terms of digital skill, youth with greater “cyber-confidence” (Shin and Ahn, 2015) and more sophisticated digital skills are more likely to participate in cyberbullying (Wang and Ngai, 2021). Utilizing digital media for more purposes, combined with advanced digital skills, can lead some youth to view digital spaces as providing opportunities for participation in mean behaviors (Rodriguez-De-Dios et al., 2018).

Figure 3.

Social-ecological model of cyberbullying.

Another important factor is a youth’s digital management strategies, such as the ability to disconnect (Price and Green, 2016) and the balance between time spent online and offline (Den Hamer and Konijn, 2016). As youth spend more time online, opportunities to perpetrate and/or experience cyberbullying increase (Sampasa-Kanyinga and Hamilton, 2015). This suggests that digital management strategies can be a mitigating factor for reducing exposure to cyberbullying (Brooks and Lasser, 2018). Yet, measuring digital management strategies can be challenging, as there is not yet a single, widely acceptable approach.

A youth’s extent of education about cyberbullying is also an individual-level factor that can influence involvement (Adorjan and Ricciardelli, 2019). This is because learning how to responsibly use digital technologies can mitigate problematic online behaviors (Kaluarachchi et al., 2020). By acknowledging that cyberbullying is a real and serious concern, youth can protect themselves from being targeted and reduce their own involvement (Cross et al., 2015; Graber, 2019). By being informed, such as through cyberbullying-related education, youth can feel more empowered, become active bystanders, and/or report incidents (Vlaanderen et al., 2020). It is important that education and awareness start at younger ages to have long-term effects (Salmivalli et al., 2021).

Microsystem

As shown in Figure 3, the microsystem encompasses the influence of a youth’s immediate social network (Price and Green, 2016), including the attitudes toward cyberbullying of peers, parents, and teachers (Wright, 2016). Parents constitute a key component of the microsystem, especially in childhood and early adolescence, because they teach children about digital media including topics such as privacy, overuse, and potential risks (Graber, 2019). Through ongoing conversations with parents, youth can better recognize cyberbullying and how to cope (e.g. seeking support, saving the evidence) (Savage et al., 2017).

Friends/peers are just as influential as parents, and as youth become more independent, their influence grows (Sasson and Mesch, 2016). The effect of friends/peers occurs through normative social influence, which describes how individuals conform to group norms in a desire to fit in and be liked by others (Schultz et al., 2008). For example, youth adjust their online engagement to the routines and practices of friends/peers (e.g. choice of platforms), and group norms toward online behaviors (Marwick and Boyd, 2014). For instance, Cross et al. (2015) found that youth with close friends who engage in or approve of cyberbullying were more likely to perceive it as acceptable. This shows how friends/peers serve as role models and can influence youth’s attitudes toward and involvement in cyberbullying (Sasson and Mesch, 2016).

Even though cyberbullying often occurs outside the classroom (Patchin and Hinduja, 2006), educators play a pivotal role in providing students with knowledge regarding online safety, digital risks, (mis)use of digital media (Baldry et al., 2018), and cyberbullying-specific information such as what to do if they are being cyberbullied (Patchin and Hinduja, 2012). This suggests that how educators discuss and respond to cyberbullying impacts students’ perceptions. For example, when educators take cyberbullying seriously, students better recognize the severity of cyberbullying, which signals to them that cyberbullying is unacceptable (Hinduja and Patchin, 2013), and they are more likely to report cyberbullying instances to educators (Cassidy et al., 2013). Thus, educators are critical in promoting cyberbullying education, prevention, and response.

The different components of the microsystem can also influence one another or work cooperatively. Teachers can offer workshops and talks about social media and cyberbullying for parents to learn about the risks and prevention strategies. For example, Alcalá et al. (2019) implemented an intervention program at a school in Spain based on cooperative learning. The whole school community including peers, parents, teachers, and the management team were involved in the intervention that consisted of supporting a cyber victim. The findings show that the intervention produced meaningful improvements in the emotional and social state of the cyber victim. This demonstrates how components of the microsystem interact with one another in complex ways, with cooperative interventions being effective.

Mesosystem

The mesosystem describes the interaction between microsystem groups (Figure 3), which can be either congruent or incongruent about what cyberbullying is and how to address it. For instance, if peers condone aggressive online behaviors, youth may be more likely to engage in these types of behaviors (Price and Green, 2016). If parents and educators intervene through education and conversations, this can prevent participation in cyberbullying (Helfrich et al., 2020). This is because parents and educators make individuals aware of the consequences of their actions (Park et al., 2021) and better equip them with the knowledge and skills to make good, informed choices when faced with pressure from their peers (Espelage and Holt, 2001). As youth grow older and become more independent, the influence of parents and teachers diminishes while that of peers increases (Sasson and Mesch, 2016). Additional research is needed to determine which microsystem groups have greater influence and how incongruence is resolved. If parents’ and educators’ efforts can lessen peer influence, this suggests education and communication are effective in moderating online aggressive behaviors that peers condone. It is important to better understand the (in)congruence among microsystem groups for identifying key mediators of cyberbullying involvement.

Exosystem

While cyberbullying research on the exosystem remains sparse (Wright, 2016), it is important to assess existing laws and policies related to cyberbullying. These considerations lead to valuable insights regarding their enforcement, which reminds youth that, contrary to what some bullies may believe (i.e. lack of accountability online), there are consequences to being a perpetrator (Hinduja and Patchin, 2019). This perception of a lack of accountability online can be attributed to the difficulty of enforcing rules online related to factors such as anonymity (making it challenging to identify the perpetrator) and online disinhibition (Hinduja and Patchin, 2018). Regarding enforceability, it is necessary to examine how parents and institutions teach and enforce cyberbullying laws and policies (Price and Green, 2016). This is because consistency helps to ensure youth receive regular and consistent messages about cyberbullying, which can mitigate involvement (Hinduja and Patchin, 2012).

As illustrated in Figure 3, the exosystem also considers the availability of tools and resources, such as features of digital environments (e.g. blocking, reporting) that digital users can take advantage of when they experience cyberbullying (Hudson et al., 2016), educational resources available online (e.g. Facebook Bullying Prevention Hub), and information regarding action and response (e.g. platform community guidelines) (Topcu-Uzer and Tanrikulu, 2018). For example, the availability of anonymous reporting resources facilitates individuals reporting cyberbullying without the risk of being exposed (Langos and Giancaspro, 2019) or being further targeted by the cyberbully (Benzmiller, 2013). Evaluating the awareness and use of cyberbullying resources offered to young digital users can lead to improving available tools and resources and implementing strategies to increase their effectiveness.

Collaborations between sectors, including, but not limited to, parents, educators, police, mental health practitioners, policymakers, and service providers, is important for cyberbullying prevention and response. Also, in the exosystem, indirect influences need to be considered such as parents’ availability due to job stress (Wernert, 2017). Referred to as a “networked response” (Broll, 2014), these collaborations can enhance support services, respond to the causes and consequences of cyberbullying, and implement more tailored and holistic training programs (UNESCO, 2020). In addition, through these collaborations, education regarding cyberbullying can provide individuals with key skills that help to mitigate cyberbullying such as conflict resolution, empathy and compassion, resilience, self-esteem building, and communication (Paolini, 2018). Thus, understanding the importance of these collaborations, and their effectiveness for cyberbullying prevention and response, is a critical area of future inquiry.

Cyberbullying experiences of others (i.e. friends/peers, mediated portrayals) can indirectly influence attitudes toward cyberbullying (Gorzig and Machackova, 2016). While youth themselves may not be directly involved, experiences of others can “frame” perceptions and evaluations of cyberbullying. Framing refers to how certain topics, events, or phenomena are presented to an audience, which can influence an individual’s point of view (Goffman, 1974). For example, a highly publicized Canadian case—the cyberbullying of teenager Amanda Todd who took her life as a result—has framed conversations around cyberbullying (Sklar, 2012). The considerable attention given to the Todd case in news media has prompted important conversations around cyberbullying, the need for support systems, and the urgency of immediate response (CBC News, 2022). Other portrayals of cyberbullying in streaming media, like the film Cyberbully, depicted cyberbullying in such a way that ignited conversations among youth around cyberbullying, particularly the impacts associated with victimization and the necessity for effective interventions. Such depictions make discussions of cyberbullying less taboo, allowing young viewers to better understand the phenomenon and what to do when it happens.

Macrosystem

The macrosystem examines changing social norms around cyberbullying and associated policy and legal frameworks at local and national levels. It also examines factors at the macrolevel including issues of access to technology such as existing infrastructure in neighborhoods, schools, and rural areas (Robinson et al., 2020). When looking at social norms, these guide perceptions around cyberbullying and the corresponding mechanisms in place for cyberbullying prevention and response (Coburn et al., 2019). For example, if the social norm about what is an acceptable behavior sees cyberbullying as a major problem that needs attention, this norm will be reflected in policies, legal frameworks, and the consequences for perpetrators (West et al., 2014). However, as digital media change and evolve, policies must be updated and evaluated to assess their relevance (Marczak and Coyne, 2016).

As Figure 3 shows, smaller-scale cultural variations, such as adult culture and youth culture, racial/ethnic differences, and socioeconomic differences are also important because they impact how different groups understand cyberbullying (Crosslin and Golman, 2014). For instance, looking at differences across generations and cohorts, parents who may not have grown up with social media face challenges understanding cyberbullying (Espelage and Hong, 2017) and the degree of impact of cyberbullying outcomes (Cassidy et al., 2012). From parents’ perspectives, this lack of understanding is attributed to insufficient opportunities to stay updated on digital media, the limited knowledge about the impacts of cyberbullying on their children, and not knowing how they can take a more active role to prevent and intervene in cyberbullying (Midamba and Moreno, 2019). Furthermore, there is a necessity to consider racial/ethnic and/or socioeconomic differences as many factors influence cyberbullying involvement (Edwards et al., 2016). For example, Xu et al. (2020) found that racial and ethnic minorities were disproportionately affected by contextual-level factors associated with bullying (e.g. adverse home and school environments), yet these individuals were protected against bullying involvement and outcomes due to strong ethnic identity, positive cultural and family values, and other resilience factors. To develop a more informed understanding of cyberbullying, smaller-scale cultural considerations can reveal potential variations in the phenomenon as well as impact the development of better targeted cyberbullying prevention and response strategies.

The macrosystem also focuses on cross-cultural views, such as how cultural norms and values influence attitudes toward digital media, understanding cyberbullying, and acceptability of and attitudes toward online behaviors (Marczak and Coyne, 2016; Price and Green, 2016). For example, in a study of African youth, Ephriam (2013) emphasized how digital media use and engaging in online behaviors like cyberbullying can challenge cultural norms and values. This work shows cultural differences in digital and social media use for perpetrating deviant and criminal online behaviors, primarily because culture is not universal and different cultures have differing norms (Ephriam, 2013). Research so far has shown that variations across cultures can lead to varying cyberbullying prevalence rates and differing conceptualizations of the phenomenon (Wright, 2016). Being attuned to cultural differences is important given that cyberbullying removes geographical boundaries (Hinduja and Patchin, 2018). However, more macrolevel research is needed, as limited studies exist examining cultural differences (Bayraktar, 2016), yet such research can uncover how to collectively combat serious outcomes for cyber victims (Muneer and Fati, 2020). While broader strategies targeting cyberbullying may not need to drastically differ across cultures (Shapka et al., 2018), being attuned to cultural variations can help tailor better cyberbullying initiatives (Mojdehi et al., 2019).

Chronosystem

The chronosystem considers time-related factors that affect cyberbullying, as shown in Figure 3. First, the chronosystem looks at how digital media have evolved and their integration into the lives of individuals (Wright, 2016) to identify changing and differing social norms around digital media use and how these can impact attitudes toward cyberbullying (Bayraktar, 2016). Following the proliferation of digital devices (i.e. smartphones) and new applications (i.e. social media like Instagram and Twitter) (Balbi and Magaudda, 2018), social media has gained immense popularity for supporting social interactions, enabling engagement within online communities, and shifting the way youth communicate (Anderson and Jiang, 2018). Therefore, by taking into consideration digital-specific changes over time, reflecting on the ways youth are using and interacting within online spaces, and examining how online interactions can lead to the facilitation of problematic social behaviors, we can better understand cyberbullying.

Second, life course transitions can affect perceptions around and engagement in cyberbullying (Cross et al., 2015). For example, in childhood and early adolescence, cyberbullying generally manifests through behaviors like mean messages, social exclusion, or having private information revealed, whereas in later life stages (e.g. older adolescence, young adulthood), cyberbullying manifests itself differently, taking place in the form of sexting, public shaming, or harassment (Myers and Cowie, 2019). As individuals move into the workforce, they encounter different types of cyberbullying such as having one’s opinions and expertise ignored and being given unreasonable tasks and/or a heavy workload (Mowry and Giumetti, 2019). These types of cyberbullying can particularly affect youth, who have limited work experience. In work settings, the consequences of being exposed every day to the bully can be damaging, as the victim can feel helpless, particularly in cases where there are power imbalances. This can lead to decreased job satisfaction and mental strain (Mowry and Giumetti, 2019). Researchers need to consider life course–related changes in cyberbullying and how they affect youth. Experiences of cyberbullying are detrimental for children and youth (Patchin and Hinduja, 2020), and evidence suggests that as cyberbullying behaviors change, evolve, and escalate, the consequences can be just as serious and damaging (Cowie and Myers, 2018).

Third, historical events and crises have changed cyberbullying. For example, despite reports suggesting positive aspects of the COVID-19 pandemic for youth (e.g. benefits of learning remote, feeling closer to family and loved ones) (Anderson et al., 2022), there have been serious downsides, particularly regarding online behaviors like cyberbullying. For instance, due to public health regulations during the COVID-19 pandemic (e.g. social distancing), youth used digital technologies for more types of activities (i.e. socializing, learning) (De et al., 2020), subsequently increasing exposure to and participation in cyberbullying (Rideout et al., 2021). While evidence suggests social support from one’s microsystem relationships helps mitigate cyberbullying outcomes (Hellfeldt et al., 2020), and that social support is vital during times of stress and uncertainty (Wang and Eccles, 2012), the availability of support may have been compromised during the pandemic due to limited in-person interactions (Rogers et al., 2021). As a result, victims faced potentially more serious outcomes, like severe depression (Michael and Reyes, 2021) and loneliness (Han et al., 2021). In sum, considering how historical events and crises impact cyberbullying provides insights into the challenges for victims and the need for additional support systems.

Digital context

The social-ecological model of cyberbullying depicted in Figure 3 shows how digital media are intertwined with many domains of life. In the model, the digital context permeates throughout all the other ecological systems, impacting how they function. Figure 3 also shows the digital context as an additional, outer layer to the model in which all other ecological systems are embedded within. This outer layer shows how some digital-specific factors relevant to cyberbullying are exclusive to the digital context because they cannot be situated within any other ecological system (McMahon, 2014). Yet, as the arrows of Figure 3 show, these digital-specific factors—while external to the other systems—can have an indirect influence (e.g. features of digital media like notifications may determine an individual’s purpose and frequency of digital media use).

In the digital context, it is important to consider the types of digital media used and their features (i.e. portability, capabilities, design) (Gorzig and Machackova, 2016; Price and Green, 2016). Since digital devices enable connectivity from almost anywhere (in high-income countries), the boundaries of where and when cyberbullying can take place have broken down (Festl et al., 2013). For example, the development of easily transportable smartphones, rise in high-speed Internet, affordability of data plans, and services like unlimited texting, expanded the range of services available, subsequently increasing opportunities for engaging in, being targeted by, and viewing cyberbullying (Sathyanarayana et al., 2018).

The digital context also identifies unique aspects of digital media that help facilitate cyberbullying, which can be linked to boyd’s (2010, 2014) characteristics of technology, including persistence, visibility, spreadability, and searchability. Digital media are persistent by design (boyd, 2010) and never truly shut off (Ito et al., 2010), which breaks down boundaries of space and time (Hinduja and Patchin, 2018). This means that unlike offline bullying, even when digital devices are turned off and platforms are signed out of, youth can still become targets of cyberbullying (Sabella et al., 2013). This heightens vulnerability and the potential harms accompanying cyberbullying (Rice et al., 2015) because online content is “durable,” permanent, and accessible on-demand (boyd, 2010). As a result, cyberbullying may never truly go away and snowball to be even more impactful (Reio and Ledesma Ortega, 2016).

Related to persistence is visibility. With social life converging online, our lives have become more public, allowing wider audiences access to more information quickly and easily (Baym and boyd, 2012). If privacy protection strategies are not used, this visibility is wider and private information could be exposed (Carrier, 2018), further increasing opportunities for cyberbullying (Adorjan and Ricciardelli, 2019). Increased visibility can magnify social conflicts, allowing audiences to not only see, but participate in the conflict (boyd, 2014), which could also increase cyberbullying occurrences (Koutamanis et al., 2015).

Visibility is enhanced by spreadability where online, audience members can see, engage with, and contribute to (e.g. commenting, liking) online content (boyd, 2014). The concern is that sharing online content increases the chances for information to spiral out of control (i.e. starting rumors) (Baym and boyd, 2012), which could lead to facilitating or worsening cyberbullying (Burgess-Proctor et al., 2010). This is especially true when content goes viral, creating an impression of the targeted individual (e.g. embarrassing or shaming them), which can impact their reputation online and offline (Rosewarne, 2016). Thus, even though individuals may be sharing content they find interesting or as a form of social currency (boyd, 2014), it could lead to and worsen cyberbullying (Patchin and Hinduja, 2014).

Reinforcing the permanence of online content, information can easily be retrieved long after it was posted with a simple search (boyd, 2014). Due to this searchability, there is an increased possibility for rumors to flourish, which can lead to and escalate cyberbullying (Ito et al., 2010). This could have long-standing impacts on one’s reputation (Bridges, 2021).

In addition to the four characteristics boyd discussed, another factor is the ability to conceal one’s identity online through anonymity, aliases, and fake accounts, which allow cyberbullies to perpetrate cyberbullying without cyber victims knowing who they are (Hinduja and Patchin, 2018). By remaining anonymous, cyberbullies may be more compelled to disclose personal or private information about their target(s), thinking that their actions have no repercussions (Hinduja and Patchin, 2015). This gives cyberbullies an immense amount of power and reduces empathy since cyberbullies cannot necessarily see the harm caused, further removing them from their actions (Rosewarne, 2016). Thus, cyberbullies protect themselves while inflicting harm onto others. However, differences should be noted between those initiating cyberbullying and those going along with the cyberbullying such as bystanders, as there may be variations in how to address different types of cyberbullies depending on their social role (Song and Oh, 2018).

Finally, the digital context also includes the online disinhibition effect, which describes a lack of restraint online compared with how individuals act and behave offline (for a full explanation of this concept, see Suler, 2004). Online disinhibition is enabled because youth are removed from the influence of in-person authority figures such as adults and social norms, causing cyberbullies to push the boundaries of acceptability because there are no rules, regulations, or cues telling them otherwise (Hinduja and Patchin, 2019). Thus, online disinhibition facilitates cyberbullying because digital media changes the nature of interactions, making certain behaviors acceptable online (Wright and Wachs, 2018). Overall, by taking the digital context into consideration, there is a better understanding of the unique aspects of digital media that help facilitate cyberbullying, and the ways the phenomenon has evolved and expanded with and alongside digital media.

Discussion

The present article develops a novel model of cyberbullying based on the existing literature that incorporates digital-specific factors within each system of the original EST model. While the model is based on Bronfenbrenner’s EST, it expands it by integrating the digital context as its own ecological system. Bronfenbrenner and Ceci (1994) stressed the importance of close, reciprocal face-to-face interactions within a youth’s immediate environment. While interactions with parents and caregivers are still considered critical for cyberbullying education and awareness, the social-ecological model of cyberbullying expands on this by stressing the relevance of weak, nonreciprocal digital interactions with other gamers, social media users, influencers, and so on. This acknowledges the relevance of the digital context, where perpetrators are often unknown. Furthermore, our model also builds on Bronfenbrenner’s refinement of the chronosystem. We argue for age-appropriate understandings of cyberbullying that consider life phases and transitions. Here, we also include historical events and crises—such as the COVID-19 pandemic—that can affect the amount of time youth spend online, tilting the balance between time spent online and offline. Therefore, the social-ecological model of cyberbullying expands and updates the model to take into consideration that digital media are an integral part of a youth’s everyday life, and online and offline spheres are not separate but rather overlap.

While scholars have previously attempted to integrate the digital context within an EST approach, debates have persisted in the literature as to where and how to situate it (Cross et al., 2015; Johnson, 2010). While some scholars have situated digital-specific factors within one of the systems of the existing model, this approach is lacking because it does not fully integrate digital-specific factors across all the systems (McMahon, 2014). For example, Sincero (2012) and Wright (2016) both integrated the digital context into the chronosystem. Yet, it is not well suited to account for all the ways that digital media influence an individual with the chronosystem’s unique focus on the passage of time and the various events and major changes that occur throughout an individual’s life. While changes in how digital media are used constitute major societal shifts, the chronosystem cannot account for the overall digital context and its many ramifications. As a result, integrating the digital context into a single ecological system does not fully capture the larger role of the digital in cyberbullying. To remedy this problem, the social-ecological model of cyberbullying emphasizes the interconnectedness of the digital context with all other systems of the ecological model as demonstrated by the arrows in Figure 3. Therefore, unlike other models that tend to focus on a subset of digital variables or omit the interconnectedness of the digital context with other ecological systems (McMahon, 2014; Tanrikulu, 2015), the proposed model embeds the ecological systems of the original EST under the digital context to allow for a more comprehensive examination of cyberbullying.

Limitations

Since the social-ecological model of cyberbullying builds on and expands EST, it shares similar limitations. Like EST, the social-ecological model of cyberbullying takes into consideration a multiplicity of factors within each ecological system, which makes it unfeasible to examine all factors in a single study (Harper et al., 2018). This means that scholars need to make decisions as to what factors, contexts, and ecological systems to include in a single study. In addition, some factors are easier to operationalize and measure than others (e.g. empathy, digital literacy), which can impact what factors researchers prioritize (Patchin and Hinduja, 2015).

Future research

The model is a first step toward consolidation of the vast literature on cyberbullying and there is much opportunity for future research, including testing, finetuning, and expanding the model. First, the model affords great flexibility, allowing scholars to test the model in ways tailored to their research questions or methodologies. For example, scholars can evaluate a specific social context such as investigating cyberbullying at post-secondary institutions. Or scholars can choose to focus on parental responses to cyberbullying across cultures. Second, scholars can work to finetune the model as new digital media emerge, and cyberbullying evolves and changes. For example, finetuning is necessary with the emergence and growing use of TikTok (Zhang and Quan-Haase, 2022), which has distinct uses and gratifications (Shao and Lee, 2020) that have led to higher rates of cyberbullying (Na, 2020). By looking at the broader digital context and the ways digital media interconnect with other ecological systems, the model does not risk becoming outdated as digital media evolve. Instead, new digital media, such as TikTok, can be investigated using the model to finetune various components such as the relation between features of platforms and cyberbullying risks. Finally, the social-ecological model of cyberbullying can be expanded to other life phases, such as young adulthood (19+), and contexts, such as the workplace. It can also guide studies of toxic online behaviors related to or under the umbrella of cyberbullying including sexting, trolling, and sextortion. Like the social-ecological model of bullying, a key contribution of the social-ecological model of cyberbullying is that it can guide effective and well-developed cyberbullying prevention, intervention, and educational programs. As the digital context remains prominent in a post-COVID-19 era (Gordon, 2020), such initiatives are much needed, as they target simultaneously a multitude of dimensions or ecological systems related to cyberbullying.

Footnotes

Authors’ note

Molly-Gloria Patel is also affiliated to Okanagan College,Canada.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This article draws on research supported by the Social Sciences and Humanities Research Council of Canada.

ORCID iDs

Molly-Gloria Patel

Anabel Quan-Haase

Author biographies

Molly-Gloria Patel (she/her) is a professor at Okanagan College in Kelowna,BC as well as a post-doctoral scholar at Western University in London,ON where she is working under Dr. Anabel Quan-Haase. Recently,she completed her Ph.D. at Western University where her dissertation centered on the examination of cyberbullying,particularly cyberbullying occurring at the post-secondary level. Her research interests center on cyberbullying,digital media,social media,youth and youth culture,deviance,and criminology. Her co-authored work has been published in prominent journals including Network Science,the Journal of Social and Personal Relationships,and Information,Communication & Society.

Dr. Anabel Quan-Haase (she/her) is a Full Professor of Sociology and Information and Media Studies and the Rogers Chair in Studies in Journalism and New Information Technology at Western University. She is the director of the SocioDigital Media Lab and her work focuses on social change,social media,and social networks,with a keen interest in novel methodologies. She is the coeditor of the Handbook of Social Media Research Methods (Sage,2022),coeditor of the Handbook of Computational Social Science (Routledge,2022),coauthor of Real-Life Sociology (Oxford University Press,2020),and author of Technology and Society (Oxford University Press,2020).

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The social-ecological model of cyberbullying: Digital media as a predominant ecology in the everyday lives of youth

Abstract

Keywords

Introduction

Background

The social-ecological model of bullying

The social-ecological model of cyberbullying

Individual level

Microsystem

Mesosystem

Exosystem

Macrosystem

Chronosystem

Digital context

Discussion

Limitations

Future research

Footnotes

Authors’ note

Funding

ORCID iDs

Author biographies

References