Table 1.
Main characteristics of the papers included in the review.
| Author/s year | Characteristics of the sample | Subject of investigation | Hate speech, (Cyber)bullying | Social media | Main findings | Paper key word |
|---|---|---|---|---|---|---|
| Mitchell et al. (2011) | 2.051 adolescents age range 10–17 years | To examine rates of victimization, and the association between online and offline victimization. In particular, the symptoms of trauma and delinquency among adolescents were analyzed | Sexual victimizations, and psychological and emotional abuse | Not specified | Results show that many victims are at risk because they have very complex previous emotional experiences. Offline victimization experiences are associated with online victimization | Internet; Victimization; Adolescents; Trauma; Delinquency; Life adversity |
| Bossler et al. (2012) | 434 adolescents students in a Kentucky middle and high school | To explore online harassment experiences. In particular, it was examined whether routine computer activities including use of computers increase the risk of victimization | Online harassment,bullying | MySpace, Facebook | Results show that having a social networking site and sharing personal information on these online platforms seemed to make harassment more likely than using tools such as email or instant messaging | Online harassment, routine activities theory, bullying |
| Ybarra et al. (2011) | 1,588 adolescents age range 10–15 years | To examine technology-mediated exposures (e.g., hate sites, death sites) and experiences (e.g., bullying) and how they are associated with psychosocial challenges (e.g., violent behavior, depressive symptomatology) | Cyberbullying, Internet harassment unwanted sexual solicitation, unwanted sexual experiences | Text-Messaging, Violent Web sites, Hate sites, Design-based, War, death, and “terrorism” sites Design-based, Cartoons sites Design-based, Violent x-rated (“adult”) sites | Results show that while youth move online, victimization rates increase. Almost all violent experiences and exposures online are caused by the use intensity and frequency of the Internet and text messaging | Youth, violent media, Internet harassment, unwanted sexual solicitation, unwanted sexual experiences, cyberbullying |
| Oksanen et al. (2014) | 723 Adolescents age range 15–18 years | To analyze data collected from a sample of Finnish Facebook and YouTube adolescent users. This research investigated the extent of exposure to and victimization by online hate material among young social media users | Hate material, victimization | Facebook and YouTube | Results show that exposure to hate material was associated with high online activity, poor attachment to family and physical offline victimization. Online hate material primarily focused on sexual orientation, physical appearance, and ethnicity | Victimization, internet, adolescents, youth, hate material |
| Pauwels and Schils (2016) | 6,020 Adolescents age range 16–24 years | To apply Social Learning theory to the explanation of political violence, focusing on exposure to extremist content through new social media and Facebook | Youth delinquency, youth delinquency, differential association exposure | Facebook, NSM | Results show that the most violent effects of new social media and Facebook are found for those measures where individuals actively seek out extremist content on the Internet. It is necessary to check background | Nonstate actors, Radicalization, Terrorism / counterterrorism, Violent extremism |
| Variables such as personality characteristics, moral values and peer influences | ||||||
| Räsänen et al. (2016) | 723 adolescents age range 15–18 years | To examine whether the risk of online hate victimization is more likely when youth visited online sites containing potentially harmful content | Victimization, online hate social media | Results show that being involved in the production of hate material and in researching such content one puts young people in danger | Social media; routine activity theory; victimization; online hate | |
| Baldry et al. (2019) | 5,058 adolescents age range 11–18 years | To investigate post-traumatic stress symptoms affecting the involvement in school bullying and cyberbullying of boys and girls according to the different bullying roles | Cyber bullying School bullying Cybervictimization | Not specified | Results show that school and cyberbullying are risk factors for development of post-traumatic stress symptoms differently affecting adolescents according to their role | School children, post-traumatic stress disorder, schools, symptoms, abuse, adolescents, aggressive behavior, boys, girls, human behavior, human diseases, risk factors, risk groups, students, children |
| Longobardi et al. (2020) | 345 adolescents age range 11–16 years | To analyze the association between Instagram popularity and subjective happiness and evaluate the relationship between roles of cyber victimization and social media addiction | Cyber victimization, Social media addiction | Results show that social media use and cyber victimization were positively correlated, and both showed a negative correlation with perceived subjective happiness | Cyber victimization, Instagram, Peer exclusion, social media addiction, Happiness, Well-being, Adolescents | |
| Blaya et al. (2020) | 1,900 students age range 12–20 years | To examine the association between school bullying and cyberhate victimization and perpetration | Cyberhate, bullying, victimization and perpetration | Not specified | Results show that bullying and cyberhate are a common experience for many young people. In particular, the overlap between bullying and cyberhate and between traditional bullying and cyberbullying is evident | Cyberhate, Young people, Victimization, Involvement, School bullying, Overlap |
| Wachs et al. (2020) | 1,480 adolescents age range 12–1ears | To investigate adolescents' coping strategies for cyberhate, while considering differences in adolescents' sex, age, socioeconomic status (SES), and victim status | Cyberhate, coping strategies, adolescents, cyberbullying | Not specified | Results show that different coping strategies are used by adolescents, with differences depending on sex, age, socioeconomic status, and victim status | Cyberhate Coping strategies Cybervictimization Hate speech Cyber discrimination |
| Ang and Goh (2010) | 396 adolescents age range 12–18 years | To examine the association between affective empathy, cognitive empathy, and gender on cyberbullying among adolescents |
Cyberbullying among adolescent (for an example, hurting someone by sending them rude text messages) | Not specified | Results show that both boys and girls who had low cognitive empathy had higher scores on cyberbullying than those who had high cognitive empathy | Cyberbullying, Affective empathy, Cognitive empathy, Gender |
| Barlett et al. (2019) | 3,079 youth participants average age 13.12 years (Wave 1) 1,957 youth participants (Wave 2) 909 youth participants (Wave 3) | To testing (a) the longitudinal stability in positive cyberbullying attitudes (CA), (b) whether any change in positive CA over time predict subsequent cyberbullying perpetration, and (c) the cross-lagged relations between positive attitudes toward CA and behavior over time | Positive cyberbullying attitudes, cyberbullying perpetration. (Relation between attitude and behavior) | Not specified | Results show a modest stability in positive CA and perpetration over time. Latent class analysis classified participants into either stable high attitudes, stable low attitudes, increasing attitudes, or decreasing attitudes | Cyberbully, cyberbullying attitudes |
| Gradinger et al. (2009) | 761 adolescents age rage 14–19 years | To analyzed whether students in the world both traditional and cyber belonging to groups of bullies or victims and bully and victims differed regarding adjustment | Traditional bullying, cyberbullying, traditional victimization, and cybervictimization | Mobile phones and computers (Not further specified) |
Results show that the highest risks for poor adjustment were observed in students who were identified as combined bully-victims (traditional and cyber). In addition, gender differences were examined | Cyberbullying, cybervictimization, adjustment, aggression, configural frequency analysis |
| Schneider et al. (2012) | 20,406 students 9th through 12th grade | To examine the prevalence of cyberbullying and school bullying victimization and their associations with psychological distress | Bullying victimization and psychological distress, including depressive symptoms, self-injury, and suicidality | Not specified | Results show that victimization was higher among no heterosexually identified youths. Victims report lower school performance and school attachment. Distress was highest among victims of both cyberbullying and school bullying | Adolescent, Bullying, Psychological epidemiology, Stress, Psychological etiology |
| Low and Espelage (2013) | 1,023 early adolescents age range 10–15 years | To understanding the role of maladaptive family social dynamics on cyber-bullying and nonphysical bullying (i.e., verbal and relational) involvement through individual risk and protective factors | Cyber-bullying and nonphysical bullying (i.e., verbal and relational) | Not specified | Findings validate the importance of familial socialization. Cyber-bullying shows a significant overlap with nonphysical bullying, in particular, nonphysical bullying levels were associated with both higher family | Bullying, cyberbullying, longitudinal predictors, race, gender |
| Violence and lower parental monitoring | ||||||
| Mehari and Farrell (2018) | 677 adolescents age range 11–15 years | To assess whether the dimensional model that cyberbullying that fits into a framework of adolescent aggression considered both form (overt or relational) and media (in-person or electronic) best fit the data | Form (overt or relational) and media (in-person or electronic) of aggression | Not specified | Results show that cyberbullying is a new form of aggression, a counterpart to overt and relational aggression, and this conceptualization fits the data quite well | Aggression, cyberbullying, adolescence, measurement of aggression, electronic media |
| Mishna et al. (2010) | 2,186 students grades 6, 7, 10, and 11 | To examine technology use, cyberbullying behaviors, and the psychosocial impact of bullying and being bullied, among a large sample of middle and high school students in a large urban center | Cyberbullying and technology use | Not specified | Results show that bullying was perpetrated by and toward friends and that bullies do not often disclose that they have been bullied. After the assault, the online bully reported feeling angry, sad, and depressed | Adolescents, Canada, cyber bullying, victimization, gender, ethnicity |
| Ortega-Ruiz et al. (2009) | 1,671adolescents age range 12–17 years | To examine the emotional impact caused to victims of traditional bullying or cyberbullying through technologies such as cell phones and the Internet | Direct bullying, indirect bullying, bullying inflicted via mobile phone, bullying inflicted via internet | Mobile phones and internet (no further specification) | Results show that traditional bullying affected more youth than cyberbullying. Cyberbullying produces emotional profiles like traditional bullying. The most common emotional response is anger and other negative emotions | Bullying, cyberbullying, emotions, victimization, adolescents |
| Patchin and Hinduja (2010) | 1,963 students age range 10–16 years | Examines the relationship between cyberbullying and their level of self-esteem in the experience of middle school students | Cyberbullying | Email, MySpce or other (not further specified) web page | Results show that students who were both victims and perpetrators in cyberbullying had significantly lower self-esteem than those who had little or no experience with cyberbullying | Aggressive behavior, behavior, human behavior, programmes, psychological factors, school buildings, school kids, schoolchildren, teenagers, United States of America |
| Schultze-Krumbholz and Scheithauer (2009) | 71 students grades 7, 8, and 10 average age 14.05 years | To identify characteristics of cyberbullies and cybervictims to be considered as potential risk and/or protective factors in a future study with a larger sample of students. Specifically, the | Cyberbullying and cybervictimization | Email, mobile phones and internet in general | Results show that higher frequency of cyberbullying compared with traditional bullying, and an overlap between cyberbullying and cybervictimization. Also, cyberbullies and cybervictims | Cyberbullying, social behavioral correlates, cybervictimization, empathy, frequency analysis |
| Research aims to assess the quality of several measurement instruments | Showed less empathy and higher relational aggression | |||||
| Ševčíková and Šmahel (2009) | Different age groups, including 223, respectively, 224 younger adolescents (age range 12–15 years) and 248, respectively, 249 older adolescents (age range 16– 19 years) | To explore the frequency of online aggressive acts (as victim and aggressor) | Cyberbullying and aggressive behavior | Not specified | Adolescents (12–19 years old) were more often the target of aggressive behavior than older respondents | Harassment cyberbullying Internet Czech Republic |
| Waasdorp et al. (2018) | 26,494 high school youth and 16,749 middle school youth | To analyze if weight status exacerbates the association between victimization and internalizing symptoms in bullied obese youth | Association between different forms of victimization, weight status, and adjustment | Not specified | Results highlight an increased risk of psychosocial adjustment problems among obese and overweight youth who are frequent victims of bullying. The odds of experiencing cyber victimization were higher than the odds of experiencing other forms of victimization | Internalization, Obesity, Overweight, Victimization, Bullying, At Risk Populations, Peers, Symptoms, Test Construction, Adolescent Characteristics, Internalizing Symptoms, Adolescence (13-17 yrs), Male, Female |
| Yang et al. (2020) | 16,237 adolescents 6th−12th grade | To explore the relationship between cyberbullying victimization (CBV), student emotional engagement, and cognitive-behavioral engagement at both the student and school level | Traditional bullying victimization, cyberbullying victimization | Not specified | The most relevant findings suggest that CBV had a small but significant positive association with emotional engagement and a small but significant negative association with cognitive-behavioral engagement | Bullying victimization, cyberbullying victimization, school climate, student engagement |
| Zaborskis et al. (2018) | 3,814 adolescents mean age 15.67 years | To analyze the prevalence of bullying and cyberbullying and their association with suicidal behavior among school-aged children in Israel, Lithuania, and Luxembourg |
Cyberbullying and suicidality | Not specified | Results show that cyberbullying is a strong predictor of adolescent suicidality | Adolescents, bullying, cyberbullying, suicidality, associations |