Predicting Cyberbullying

Research, Theory, and Intervention

Christopher Paul Barlett

• Publisher: Academic Press
• Year: 2019
• ISBN:
  9780128166550
• Topic: Psychology
• Subtopic: Social Psychology
• Index: Psychology

Predicting Cyberbullying: Research, Theory, and Intervention delves into the theoretical advances that have been made to predict cyberbullying perpetration. It examines myriad psychological- and communication-based theories, discusses the relevant research to support (or not) each theory, and elucidates the strengths and limitations of these theories. Moreover, the book differentiates cyberbullying from traditional bullying to expand on a theory that takes such differences into account to predict perpetration. In addition, it adapts interventions to address these nuanced theoretical advancements and concludes with an examination of validated psychological theories that can inform interventions and reduce cyberbullying.

The book is an effective and concise reference for psychologists, school administrators, counselors and psychological researchers looking to understand theory and interventions for cyberbullies.

• Focuses on the cyberbully perpetrator
• Balances theory with interventional applications
• Identifies key risk factors in those who cyberbully
• Explores the scope of theoretically driven hypotheses specific to cyberbullying

Part I

Cyberbullying in a Descriptive Context

Outline

• Chapter 1 Cyberbullying in Context
• Chapter 2 Cyberbullying, Traditional Bullying, and Aggression: A Complicated Relationship
• Chapter 3 Correlates of Cyberbullying

Chapter 1

Cyberbullying in Context

Abstract

Cyberbullying perpetration is a pervasive social behavior that can cause many negative psychological, behavioral, and health outcomes for cyberbulling victims. Research has shown that cyberbullying occurs all over the world, across the developmental life span, and for both males and females. Understanding the variables and processes that predict cyberbullying perpetration is important for interventions aimed at reducing online, antisocial behavior. The purpose of this chapter is to introduce the reader to definitions of cyberbullying and discuss the prevalence rates of cyberbullying perpetration. Myriad theoretical and conceptual issues that confound the majority of definitions have serious implications for how cyberbullying perpetration is measured and its subsequent prevalence rates. These issues are discussed in this chapter.

Keywords

Cyberbullying; prevalence rates; definitions; cyber-aggression; measurement

The Internet boom of the late 1990s and early 2000s sparked a worldwide technological revolution. The creation of the Internet juxtaposed with the affordability of personal computers (PCs) sparked a new era of information gathering and sharing. Fig. 1.1 shows a graph of the average consumer price index for all urban consumers for PCs and peripheral equipment annually¹ and the percentage of the world population that had access to, and used, the Internet from 1998 to 2016.² The results clearly show that the cost of a PC decreased dramatically while the number of worldwide Internet users increased over the same time frame. Statistical analysis yields a correlation of r=−0.79, P<.001, despite the inverted logarithmic function of the data on the cost of PCs.

Figure 1.1 The worldwide adjusted consumer price index for all urban consumers for personal computers and peripheral equipment and the percentage of the population that were Internet users from 1998 to 2016.

Although not explicitly captured in Fig. 1.1, the creation and marketing of mobile phones, laptops, and tablets that allow for Internet access has only exacerbated this effect. Statistics on Internet use further highlight the pervasiveness of this social phenomenon. Indeed, Perrin and Jiang (2018) reported that 77% of US adults go online daily and that 89% from the same population group are online daily if they own a mobile device with Internet connectivity. Recent data (from the second quarter of 2017) showed that youth spend 250 and 229 minutes daily online using their mobile device and PC, laptop, or tablet, respectively; emerging adults are online 223 and 242 minutes daily on their mobile device and PC, laptop, or tablet, respectively.³ Overall, these results suggest that the Internet is used extensively.

As expected, this technological revolution sparked many positive changes in society. Indeed, the near instantaneous acquisition and sharing of knowledge has implications in a variety of fields, including medicine, education, agriculture, transportation, politics, military, government, global industry, economics, and others. From a psychological perspective, the Internet has been shown to have positive effects. For instance, the Internet-Enhanced Self-Disclosure Hypothesis posits that online communication can increase online self-disclosure, which, in turn, increases one’s number of higher-quality social relationships that eventually increase one’s psychological well-being (Valkenburg & Peter, 2009). Additionally, Groshek (2009) sampled data from 152 countries between 1994 and 2003 and showed that Internet diffusion positively predicted democracy support, the gross national income, educational enrollment, and urbanization, and negatively correlated with sociopolitical instability.

Unfortunately, whenever a new technology is introduced into a society there are also negative societal consequences that often overshadow the positives, and many of the negative consequences are unintended. Illegal copying and distribution of copyrighted material, the ease of access to (and dispersion of) illegal pornography, hacking of people’s banking and identity information, and the dissemination of fake “facts” and “news” are just a few examples of how the Internet can be a tool for antisocial and unethical behavior. Although these are important topics of study, this book is focused solely on cyberbullying—another potential negative consequence of online communication.

Defining Cyberbullying

The definition of cyberbullying is, and should be, constantly changing, which makes it difficult to define (cf., Langos, 2012). Fluid definitions of cyberbullying are expected considering that the methods by which people can harm others online shift in accordance with the availability and popularity of technology. Initially, researchers applied the three characteristics of traditional bullying (i.e., intention, power imbalance, and repetition) to the online world to define cyberbullying (see Englander, Donnerstein, Kowalski, Lin, & Parti, 2018; Kowalski, Giumetti, Schroeder, & Lattanner, 2014; Patchin & Hinduja, 2006). I will discuss whether this is warranted in Chapter 2, Cyberbullying, Bullying, and Aggression: A Complicated Relationship. For the purposes of this book, I will employ the definition by Tokunaga (2010) who defined cyberbullying perpetration as “any behavior performed through electronic or digital media by individuals or groups that repeatedly communicates hostile or aggressive messages intended to inflict harm or discomfort on others” (p. 278).

There are advantages to this definition because it accounts for the vast majority of behaviors that would constitute cyberbullying while being void of any specific technologies or Internet platforms. Having a consistent definition of cyberbullying is incredibly important for how we measure cyberbullying perpetration on questionnaires and interviews. Rivers and Noret (2010) correctly argued that the way in which cyberbullying is defined for research participants can significantly impact the reported prevalence rates. For instance, Ybarra, Boyd, Korchmaros, and Oppenheim (2012) randomly assigned participants to complete prevalence rates of online bullying perpetration (among other behaviors) under one of four conditions: (1) a formal definition of bullying including the word “bully”; (2) a formal definition of bullying without the word “bully”; (3) void of a formal definition of bullying but using the word “bully”; or (4) no formal definition and no use of the word “bully.” The results are depicted in Fig. 1.2. As can be seen, when the word “bully” is omitted, independent of whether the definition of bullying is present or not, the percentage of youth who disclose they have never cyberbullied decreases from 84%, 86% to 76%, 74%.

Figure 1.2 Prevalence of online bullying as a function of its definition and the word “bully” is present. Adapted from Ybarra, M. L., Boyd, D., Korchmaros, J., & Oppenheim, J. (2012). Defining and measuring cyberbullying within the larger context of bullying victimization. Journal of Adolescent Health, 51, 53–58. doi:10.1016/j.jadohealth.2011.12.031.

Prevalence Rates and Issues With Measurement

Researchers and laypeople alike often use prevalence rates as a way to support claims regarding cyberbullying perpetration trends, such as cyberbullying perpetration changes across time and whether there are group differences in cyberbullying perpetration. Overall, I support the use of prevalence rates when they are used to meaningfully describe the context of a social issue and when done appropriately. There are four issues that warrant concern when using or interpreting prevalence rates:

Issue 1. Prevalence rates are confounded with measurement (cf., Rivers & Noret, 2010). There is extraordinarily high variability across studies in the prevalence rates of cyberbullying perpetration. Indeed, some studies have shown that 3% of youth cyberbullied others (Bauman, 2010), whereas others have shown a much higher cyberbullying prevalence from the same youth population (i.e., 26%; Beran & Li, 2005). These percentages exemplify how disparate prevalence rates can be when different measurements are used. Of course, other methodological differences between these two cited studies could also affect the prevalence (e.g., age and location of the sample). How cyberbullying is assessed matters greatly. The data in Fig. 1.2 highlights these claims.

Issue 2. Prevalence rates from small samples are not meaningful. Although hinted at by Rivers and Noret (2010), another issue is that interpreting prevalence rates derived from small samples to apply to a much larger population is ill-advised. Rarely do researchers have the ability to get a sample size large enough to reliably approximate the population, which leads to error when attempting to make such inferences. However, there are exceptions. For instance, Elledge et al. (2013) sampled 16,634 youth from 1043 classrooms across 146 Finnish schools and showed that 4% cyberbullied others. Similarly, Laftman, Modin, and Ostberg (2013) sampled 22,544 youth from 1147 classrooms across 324 Swedish schools and showed that 5.9% cyberbullied others. In contrast, Schultze-Krumbholz and Scheithauer (2009) sampled 71 German youth and showed that 16.9% were cyberbullies. In terms of generalizing the results from a sample to a population, the former two studies warrant more confidence because the authors sampled more of the population.

Issue 3. Prevalence rates are used to create artificial groups. If responses to a cyberbullying questionnaire are used to determine the prevalence rates, then, logically, a related concern consists of researchers using responses to create artificial groups for their studies. Consistent with the traditional bullying literature, researchers will often separate participants into one of four groups (this is the traditional approach, although other groups are likely to exist): (1) bullies (those who only harm others); (2) victims (those who are only victimized); (3) bully-victims (those who harm others and are victimized); and (4) not involved (those who neither bully nor are victimized). Findings using such methods have shown group differences in self-esteem (Austin & Joseph, 1996), personality (Mynard & J...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• Preface
• Part I: Cyberbullying in a Descriptive Context
• Part II: Cyberbullying in a Theoretical Context
• Part III: Cyberbullying in an Intervention Context
• References
• Index