Overview of cyberbullying definitions, prevalence, and key findings from key studies.
| . | Cyberbullying overview . | Impact and analysis . | ||
|---|---|---|---|---|
| Author(s) and year . | Cyberbullying definitionsa . | Key findings . | Prevalenceb . | Study limitations . |
| Abaido [61] – 2020 | Use of social media to harass and intimidate others | Significant relationship: media and CB incidents | 40% of university students report being cyberbullied | Limited to one geographic region (UAE), self-reported data |
| Aboujaoude et al. [8] – 2015 | Repeated, intentional harm via electronic communication | Highlights psychological toll of CB on adolescents | 20%–40% of adolescents report frequent exposure to CB | Lacks empirical data, primarily a review paper |
| Allen and Philips [3] – 2018 | In context of anonymity and online communication | Anonymity encourages CB: lack of accountability | 30% of participants agree that anonymity increases CB | Limited sample size, focus on anonymity rather than broader CB behaviours |
| Barlett et al. [4] – 2018 | In context with anonymity and online communication | Anonymity increases likelihood: CB in anonymous sites | 25% increase in CB on anonymous platforms | Focuses only on select platforms, lacks longitudinal analysis |
| Bastiaensens et al. [49] – 2014 | Aggressive behaviour via digital means | Bystanders more likely: reinforce cyberbully than cybervictims | 35% of bystanders reported being more likely to reinforce CB | Does not consider the long-term impact of bystander behaviour |
| Berne et al. [23] – 2013 | Aggressive, repeated actions intent, and harm via technology | Correlation: depressive symptoms among adolescents | 20% adolescents report frequent CB | Cross-sectional design, self-report bias, lack of longitudinal data |
| Chun et al. [9] – 2020 | Defined inconsistently across studies | Measurement inconsistencies across different countries | Prevalence rates varied significantly by measurement tools used | Lack of standardization in measurement tools limits comparability |
| Cohen-Almagor [28] – 2018 | In context of anonymity and online harassment | Social media anonymity: contributes to the proliferation of CB | 30%–50% prevalence, dependent on anonymity | Focus on ethical responsibility, lacks empirical analysis |
| Dynel [68] – 2021 | Distinguishes CB from mock aggression | Differentiates between CB and mock aggression based on intent | Higher prevalence of mock aggression reported in humorous contexts | Lack of focus on the emotional impact of mock aggression |
| Notar et al. [63] – 2013 | Repetition, intent, and power imbalance | CB reporting; higher prevalence among females | 18% of students | Focus on specific geographic region, sample size limited generalizability |
| Ferrara et al. [15] – 2018 | A modern form of bullying facilitated by technology | Highlights health and social problems linked to CB | High prevalence of CB among adolescents | Focuses primarily on the health impacts, lacks intervention strategies |
| Gajda et al. [42] – 2022 | In context of moral disengagement | Moral disengagement: significantly mediates the relationship between: Dark Tetrad and CB | 18%–22% prevalence rate depending on Dark Tetrad traits | Cross-sectional design, lacks longitudinal data, limited to one demographic |
| Giumetti and Kowalski [11] – 2022 | Negative interaction on social media affecting well-being | Linked social media use with decreased well-being in CB victims | 15%–30% of users experienced well-being issues due to CB | Focuses on well-being impact, lacks intervention strategies |
| Gu et al. [43] – 2022 | In context curvilinear relationships victimization/social media | Highlights complex relationships with: previous CB victimization and ongoing social media use | Varied based on user response to CB incidents | Limited by self-report data, does not consider all types of social media use |
| Li and Peng [34] – 2022 | In the context of strain theory and morality/CB roles | Strain and constraints significant predictors: of CB behaviour | 20%–30% involvement in CB behaviours among adolescents | Does not consider other contributing factors beyond strain and morality |
| Lo Cricchio et al. [54] – 2021 | In the context of moral disengagement | Moral disengagement strongly linked to CB behaviours | 15%–25% prevalence among adolescents with high moral disengagement | Focus on moral disengagement, lacks analysis of other personality factors |
| Müller et al. [38] – 2018 | In the context of social media use/relationship with CB | Measurement Inconsistencies of CB across different countries | 15%–25% of participants engaged in cyberbullying behaviours | Longitudinal study, but limited to specific age groups |
| Olweus [72] – 2013 | In the context of comparative discussion: school bullying/CB | Emphasizes the similarity between traditional bullying and CB. | Prevalence rates varied depending on study, generally high | Focuses on school settings, lacks data on adult victims or other environments |
| Olweus and Limber [35] – 2018 | Similar to traditional bullying with the addition of technology | 15% of students experienced CB more than once | 15% of students | Narrow focus on the adolescent population, no data on adult victims |
| Patchin and Hinduja [24] – 2015 | Intent, repetition/harm conducted on digital platforms | Correlated CB with psychological distress and anxiety | Varied between 10% and 40%, depending on demographics | Lack of diversity in the sample population, regional focus limits generalizability |
| Sabella et al. [46] – 2013 | Emphasis on anonymity and persistent harassment | Strong link between CB, low self-esteem and anxiety | 30% of participants reported CB experiences | Self-report data subject to bias, limited focus on long-term consequences |
| Washington [25] – 2015 | Use of digital tools to harass, threaten, or humiliate | Higher levels of victimization reported among marginalized groups | 35% of marginalized participants experienced CB | Small sample size, limited focus on coping mechanisms |
| Watts et al. [26] – 2017 | Emphasizes repetitive nature of CB with power imbalance and harm | Social media cyberbullying; higher prevalence in females | 25% among social media users | Lack of cross-cultural analysis, no longitudinal follow-up |
| This paper – 2024 | Repetition, power-imbalance/goal-directed behaviour, intent, and harm | A global phenomenon, influenced by several factors: environmental/social roles/behavioural patterns/personalities/dark personality traits and anonymity versus privacy. | Prevalence rates varied across all studies. | Empirical studies are scarce across the emerging adult/adult population/Intervention/prevention support strategies lacking in young adult/adult studies. |
| . | Cyberbullying overview . | Impact and analysis . | ||
|---|---|---|---|---|
| Author(s) and year . | Cyberbullying definitionsa . | Key findings . | Prevalenceb . | Study limitations . |
| Abaido [61] – 2020 | Use of social media to harass and intimidate others | Significant relationship: media and CB incidents | 40% of university students report being cyberbullied | Limited to one geographic region (UAE), self-reported data |
| Aboujaoude et al. [8] – 2015 | Repeated, intentional harm via electronic communication | Highlights psychological toll of CB on adolescents | 20%–40% of adolescents report frequent exposure to CB | Lacks empirical data, primarily a review paper |
| Allen and Philips [3] – 2018 | In context of anonymity and online communication | Anonymity encourages CB: lack of accountability | 30% of participants agree that anonymity increases CB | Limited sample size, focus on anonymity rather than broader CB behaviours |
| Barlett et al. [4] – 2018 | In context with anonymity and online communication | Anonymity increases likelihood: CB in anonymous sites | 25% increase in CB on anonymous platforms | Focuses only on select platforms, lacks longitudinal analysis |
| Bastiaensens et al. [49] – 2014 | Aggressive behaviour via digital means | Bystanders more likely: reinforce cyberbully than cybervictims | 35% of bystanders reported being more likely to reinforce CB | Does not consider the long-term impact of bystander behaviour |
| Berne et al. [23] – 2013 | Aggressive, repeated actions intent, and harm via technology | Correlation: depressive symptoms among adolescents | 20% adolescents report frequent CB | Cross-sectional design, self-report bias, lack of longitudinal data |
| Chun et al. [9] – 2020 | Defined inconsistently across studies | Measurement inconsistencies across different countries | Prevalence rates varied significantly by measurement tools used | Lack of standardization in measurement tools limits comparability |
| Cohen-Almagor [28] – 2018 | In context of anonymity and online harassment | Social media anonymity: contributes to the proliferation of CB | 30%–50% prevalence, dependent on anonymity | Focus on ethical responsibility, lacks empirical analysis |
| Dynel [68] – 2021 | Distinguishes CB from mock aggression | Differentiates between CB and mock aggression based on intent | Higher prevalence of mock aggression reported in humorous contexts | Lack of focus on the emotional impact of mock aggression |
| Notar et al. [63] – 2013 | Repetition, intent, and power imbalance | CB reporting; higher prevalence among females | 18% of students | Focus on specific geographic region, sample size limited generalizability |
| Ferrara et al. [15] – 2018 | A modern form of bullying facilitated by technology | Highlights health and social problems linked to CB | High prevalence of CB among adolescents | Focuses primarily on the health impacts, lacks intervention strategies |
| Gajda et al. [42] – 2022 | In context of moral disengagement | Moral disengagement: significantly mediates the relationship between: Dark Tetrad and CB | 18%–22% prevalence rate depending on Dark Tetrad traits | Cross-sectional design, lacks longitudinal data, limited to one demographic |
| Giumetti and Kowalski [11] – 2022 | Negative interaction on social media affecting well-being | Linked social media use with decreased well-being in CB victims | 15%–30% of users experienced well-being issues due to CB | Focuses on well-being impact, lacks intervention strategies |
| Gu et al. [43] – 2022 | In context curvilinear relationships victimization/social media | Highlights complex relationships with: previous CB victimization and ongoing social media use | Varied based on user response to CB incidents | Limited by self-report data, does not consider all types of social media use |
| Li and Peng [34] – 2022 | In the context of strain theory and morality/CB roles | Strain and constraints significant predictors: of CB behaviour | 20%–30% involvement in CB behaviours among adolescents | Does not consider other contributing factors beyond strain and morality |
| Lo Cricchio et al. [54] – 2021 | In the context of moral disengagement | Moral disengagement strongly linked to CB behaviours | 15%–25% prevalence among adolescents with high moral disengagement | Focus on moral disengagement, lacks analysis of other personality factors |
| Müller et al. [38] – 2018 | In the context of social media use/relationship with CB | Measurement Inconsistencies of CB across different countries | 15%–25% of participants engaged in cyberbullying behaviours | Longitudinal study, but limited to specific age groups |
| Olweus [72] – 2013 | In the context of comparative discussion: school bullying/CB | Emphasizes the similarity between traditional bullying and CB. | Prevalence rates varied depending on study, generally high | Focuses on school settings, lacks data on adult victims or other environments |
| Olweus and Limber [35] – 2018 | Similar to traditional bullying with the addition of technology | 15% of students experienced CB more than once | 15% of students | Narrow focus on the adolescent population, no data on adult victims |
| Patchin and Hinduja [24] – 2015 | Intent, repetition/harm conducted on digital platforms | Correlated CB with psychological distress and anxiety | Varied between 10% and 40%, depending on demographics | Lack of diversity in the sample population, regional focus limits generalizability |
| Sabella et al. [46] – 2013 | Emphasis on anonymity and persistent harassment | Strong link between CB, low self-esteem and anxiety | 30% of participants reported CB experiences | Self-report data subject to bias, limited focus on long-term consequences |
| Washington [25] – 2015 | Use of digital tools to harass, threaten, or humiliate | Higher levels of victimization reported among marginalized groups | 35% of marginalized participants experienced CB | Small sample size, limited focus on coping mechanisms |
| Watts et al. [26] – 2017 | Emphasizes repetitive nature of CB with power imbalance and harm | Social media cyberbullying; higher prevalence in females | 25% among social media users | Lack of cross-cultural analysis, no longitudinal follow-up |
| This paper – 2024 | Repetition, power-imbalance/goal-directed behaviour, intent, and harm | A global phenomenon, influenced by several factors: environmental/social roles/behavioural patterns/personalities/dark personality traits and anonymity versus privacy. | Prevalence rates varied across all studies. | Empirical studies are scarce across the emerging adult/adult population/Intervention/prevention support strategies lacking in young adult/adult studies. |
aDefinitions as discussed in the literature (varying across several studies).
bPercentage of participants reporting cyberbullying (varied across studies).
Overview of cyberbullying definitions, prevalence, and key findings from key studies.
| . | Cyberbullying overview . | Impact and analysis . | ||
|---|---|---|---|---|
| Author(s) and year . | Cyberbullying definitionsa . | Key findings . | Prevalenceb . | Study limitations . |
| Abaido [61] – 2020 | Use of social media to harass and intimidate others | Significant relationship: media and CB incidents | 40% of university students report being cyberbullied | Limited to one geographic region (UAE), self-reported data |
| Aboujaoude et al. [8] – 2015 | Repeated, intentional harm via electronic communication | Highlights psychological toll of CB on adolescents | 20%–40% of adolescents report frequent exposure to CB | Lacks empirical data, primarily a review paper |
| Allen and Philips [3] – 2018 | In context of anonymity and online communication | Anonymity encourages CB: lack of accountability | 30% of participants agree that anonymity increases CB | Limited sample size, focus on anonymity rather than broader CB behaviours |
| Barlett et al. [4] – 2018 | In context with anonymity and online communication | Anonymity increases likelihood: CB in anonymous sites | 25% increase in CB on anonymous platforms | Focuses only on select platforms, lacks longitudinal analysis |
| Bastiaensens et al. [49] – 2014 | Aggressive behaviour via digital means | Bystanders more likely: reinforce cyberbully than cybervictims | 35% of bystanders reported being more likely to reinforce CB | Does not consider the long-term impact of bystander behaviour |
| Berne et al. [23] – 2013 | Aggressive, repeated actions intent, and harm via technology | Correlation: depressive symptoms among adolescents | 20% adolescents report frequent CB | Cross-sectional design, self-report bias, lack of longitudinal data |
| Chun et al. [9] – 2020 | Defined inconsistently across studies | Measurement inconsistencies across different countries | Prevalence rates varied significantly by measurement tools used | Lack of standardization in measurement tools limits comparability |
| Cohen-Almagor [28] – 2018 | In context of anonymity and online harassment | Social media anonymity: contributes to the proliferation of CB | 30%–50% prevalence, dependent on anonymity | Focus on ethical responsibility, lacks empirical analysis |
| Dynel [68] – 2021 | Distinguishes CB from mock aggression | Differentiates between CB and mock aggression based on intent | Higher prevalence of mock aggression reported in humorous contexts | Lack of focus on the emotional impact of mock aggression |
| Notar et al. [63] – 2013 | Repetition, intent, and power imbalance | CB reporting; higher prevalence among females | 18% of students | Focus on specific geographic region, sample size limited generalizability |
| Ferrara et al. [15] – 2018 | A modern form of bullying facilitated by technology | Highlights health and social problems linked to CB | High prevalence of CB among adolescents | Focuses primarily on the health impacts, lacks intervention strategies |
| Gajda et al. [42] – 2022 | In context of moral disengagement | Moral disengagement: significantly mediates the relationship between: Dark Tetrad and CB | 18%–22% prevalence rate depending on Dark Tetrad traits | Cross-sectional design, lacks longitudinal data, limited to one demographic |
| Giumetti and Kowalski [11] – 2022 | Negative interaction on social media affecting well-being | Linked social media use with decreased well-being in CB victims | 15%–30% of users experienced well-being issues due to CB | Focuses on well-being impact, lacks intervention strategies |
| Gu et al. [43] – 2022 | In context curvilinear relationships victimization/social media | Highlights complex relationships with: previous CB victimization and ongoing social media use | Varied based on user response to CB incidents | Limited by self-report data, does not consider all types of social media use |
| Li and Peng [34] – 2022 | In the context of strain theory and morality/CB roles | Strain and constraints significant predictors: of CB behaviour | 20%–30% involvement in CB behaviours among adolescents | Does not consider other contributing factors beyond strain and morality |
| Lo Cricchio et al. [54] – 2021 | In the context of moral disengagement | Moral disengagement strongly linked to CB behaviours | 15%–25% prevalence among adolescents with high moral disengagement | Focus on moral disengagement, lacks analysis of other personality factors |
| Müller et al. [38] – 2018 | In the context of social media use/relationship with CB | Measurement Inconsistencies of CB across different countries | 15%–25% of participants engaged in cyberbullying behaviours | Longitudinal study, but limited to specific age groups |
| Olweus [72] – 2013 | In the context of comparative discussion: school bullying/CB | Emphasizes the similarity between traditional bullying and CB. | Prevalence rates varied depending on study, generally high | Focuses on school settings, lacks data on adult victims or other environments |
| Olweus and Limber [35] – 2018 | Similar to traditional bullying with the addition of technology | 15% of students experienced CB more than once | 15% of students | Narrow focus on the adolescent population, no data on adult victims |
| Patchin and Hinduja [24] – 2015 | Intent, repetition/harm conducted on digital platforms | Correlated CB with psychological distress and anxiety | Varied between 10% and 40%, depending on demographics | Lack of diversity in the sample population, regional focus limits generalizability |
| Sabella et al. [46] – 2013 | Emphasis on anonymity and persistent harassment | Strong link between CB, low self-esteem and anxiety | 30% of participants reported CB experiences | Self-report data subject to bias, limited focus on long-term consequences |
| Washington [25] – 2015 | Use of digital tools to harass, threaten, or humiliate | Higher levels of victimization reported among marginalized groups | 35% of marginalized participants experienced CB | Small sample size, limited focus on coping mechanisms |
| Watts et al. [26] – 2017 | Emphasizes repetitive nature of CB with power imbalance and harm | Social media cyberbullying; higher prevalence in females | 25% among social media users | Lack of cross-cultural analysis, no longitudinal follow-up |
| This paper – 2024 | Repetition, power-imbalance/goal-directed behaviour, intent, and harm | A global phenomenon, influenced by several factors: environmental/social roles/behavioural patterns/personalities/dark personality traits and anonymity versus privacy. | Prevalence rates varied across all studies. | Empirical studies are scarce across the emerging adult/adult population/Intervention/prevention support strategies lacking in young adult/adult studies. |
| . | Cyberbullying overview . | Impact and analysis . | ||
|---|---|---|---|---|
| Author(s) and year . | Cyberbullying definitionsa . | Key findings . | Prevalenceb . | Study limitations . |
| Abaido [61] – 2020 | Use of social media to harass and intimidate others | Significant relationship: media and CB incidents | 40% of university students report being cyberbullied | Limited to one geographic region (UAE), self-reported data |
| Aboujaoude et al. [8] – 2015 | Repeated, intentional harm via electronic communication | Highlights psychological toll of CB on adolescents | 20%–40% of adolescents report frequent exposure to CB | Lacks empirical data, primarily a review paper |
| Allen and Philips [3] – 2018 | In context of anonymity and online communication | Anonymity encourages CB: lack of accountability | 30% of participants agree that anonymity increases CB | Limited sample size, focus on anonymity rather than broader CB behaviours |
| Barlett et al. [4] – 2018 | In context with anonymity and online communication | Anonymity increases likelihood: CB in anonymous sites | 25% increase in CB on anonymous platforms | Focuses only on select platforms, lacks longitudinal analysis |
| Bastiaensens et al. [49] – 2014 | Aggressive behaviour via digital means | Bystanders more likely: reinforce cyberbully than cybervictims | 35% of bystanders reported being more likely to reinforce CB | Does not consider the long-term impact of bystander behaviour |
| Berne et al. [23] – 2013 | Aggressive, repeated actions intent, and harm via technology | Correlation: depressive symptoms among adolescents | 20% adolescents report frequent CB | Cross-sectional design, self-report bias, lack of longitudinal data |
| Chun et al. [9] – 2020 | Defined inconsistently across studies | Measurement inconsistencies across different countries | Prevalence rates varied significantly by measurement tools used | Lack of standardization in measurement tools limits comparability |
| Cohen-Almagor [28] – 2018 | In context of anonymity and online harassment | Social media anonymity: contributes to the proliferation of CB | 30%–50% prevalence, dependent on anonymity | Focus on ethical responsibility, lacks empirical analysis |
| Dynel [68] – 2021 | Distinguishes CB from mock aggression | Differentiates between CB and mock aggression based on intent | Higher prevalence of mock aggression reported in humorous contexts | Lack of focus on the emotional impact of mock aggression |
| Notar et al. [63] – 2013 | Repetition, intent, and power imbalance | CB reporting; higher prevalence among females | 18% of students | Focus on specific geographic region, sample size limited generalizability |
| Ferrara et al. [15] – 2018 | A modern form of bullying facilitated by technology | Highlights health and social problems linked to CB | High prevalence of CB among adolescents | Focuses primarily on the health impacts, lacks intervention strategies |
| Gajda et al. [42] – 2022 | In context of moral disengagement | Moral disengagement: significantly mediates the relationship between: Dark Tetrad and CB | 18%–22% prevalence rate depending on Dark Tetrad traits | Cross-sectional design, lacks longitudinal data, limited to one demographic |
| Giumetti and Kowalski [11] – 2022 | Negative interaction on social media affecting well-being | Linked social media use with decreased well-being in CB victims | 15%–30% of users experienced well-being issues due to CB | Focuses on well-being impact, lacks intervention strategies |
| Gu et al. [43] – 2022 | In context curvilinear relationships victimization/social media | Highlights complex relationships with: previous CB victimization and ongoing social media use | Varied based on user response to CB incidents | Limited by self-report data, does not consider all types of social media use |
| Li and Peng [34] – 2022 | In the context of strain theory and morality/CB roles | Strain and constraints significant predictors: of CB behaviour | 20%–30% involvement in CB behaviours among adolescents | Does not consider other contributing factors beyond strain and morality |
| Lo Cricchio et al. [54] – 2021 | In the context of moral disengagement | Moral disengagement strongly linked to CB behaviours | 15%–25% prevalence among adolescents with high moral disengagement | Focus on moral disengagement, lacks analysis of other personality factors |
| Müller et al. [38] – 2018 | In the context of social media use/relationship with CB | Measurement Inconsistencies of CB across different countries | 15%–25% of participants engaged in cyberbullying behaviours | Longitudinal study, but limited to specific age groups |
| Olweus [72] – 2013 | In the context of comparative discussion: school bullying/CB | Emphasizes the similarity between traditional bullying and CB. | Prevalence rates varied depending on study, generally high | Focuses on school settings, lacks data on adult victims or other environments |
| Olweus and Limber [35] – 2018 | Similar to traditional bullying with the addition of technology | 15% of students experienced CB more than once | 15% of students | Narrow focus on the adolescent population, no data on adult victims |
| Patchin and Hinduja [24] – 2015 | Intent, repetition/harm conducted on digital platforms | Correlated CB with psychological distress and anxiety | Varied between 10% and 40%, depending on demographics | Lack of diversity in the sample population, regional focus limits generalizability |
| Sabella et al. [46] – 2013 | Emphasis on anonymity and persistent harassment | Strong link between CB, low self-esteem and anxiety | 30% of participants reported CB experiences | Self-report data subject to bias, limited focus on long-term consequences |
| Washington [25] – 2015 | Use of digital tools to harass, threaten, or humiliate | Higher levels of victimization reported among marginalized groups | 35% of marginalized participants experienced CB | Small sample size, limited focus on coping mechanisms |
| Watts et al. [26] – 2017 | Emphasizes repetitive nature of CB with power imbalance and harm | Social media cyberbullying; higher prevalence in females | 25% among social media users | Lack of cross-cultural analysis, no longitudinal follow-up |
| This paper – 2024 | Repetition, power-imbalance/goal-directed behaviour, intent, and harm | A global phenomenon, influenced by several factors: environmental/social roles/behavioural patterns/personalities/dark personality traits and anonymity versus privacy. | Prevalence rates varied across all studies. | Empirical studies are scarce across the emerging adult/adult population/Intervention/prevention support strategies lacking in young adult/adult studies. |
aDefinitions as discussed in the literature (varying across several studies).
bPercentage of participants reporting cyberbullying (varied across studies).
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