The DOI matches its Crossref record; 25 of the 27 references it cites were found in a registry or an index.
Defining and Measuring Cyberbullying Within the Larger Context of Bullying Victimization
Open accessAbstract
Purpose To inform the scientific debate about bullying, including cyberbullying, measurement. Methods Two split-form surveys were conducted online among 6-17-year-olds (n = 1,200 each) to inform recommendations for cyberbullying measurement. Results Measures that use the word "bully" result in prevalence rates similar to each other, irrespective of whether a definition is included, whereas measures not using the word "bully" are similar to each other, irrespective of whether a definition is included. A behavioral list of bullying experiences without either a definition or the word "bully" results in higher prevalence rates and likely measures experiences that are beyond the definition of "bullying." Follow-up questions querying differential power, repetition, and bullying over time were used to examine misclassification. The measure using a definition but not the word "bully" appeared to have the highest rate of false positives and, therefore, the highest rate of misclassification. Across two studies, an average of 25% reported being bullied at least monthly in person compared with an average of 10% bullied online, 7% via telephone (cell or landline), and 8% via text messaging. Conclusions Measures of bullying among English-speaking individuals in the United States should include the word "bully" when possible. The definition may be a useful tool for researchers, but results suggest that it does not necessarily yield a more rigorous measure of bullying victimization. Directly measuring aspects of bullying (i.e., differential power, repetition, over time) reduces misclassification. To prevent double counting across domains, we suggest the following distinctions: mode (e.g., online, in-person), type (e.g., verbal, relational), and environment (e.g., school, home). We conceptualize cyberbullying as bullying communicated through the online mode.
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“Where intention, repetition or an imbalance of power are absent from cyberbullying research, such behaviors may be more appropriately classified as cyberaggression, given that these criteria are central to differentiate bullying from aggression more broadly (15, 24, 70).”
From A thematic analysis of adolescents’ perceptions of cyberbullying and cyberaggression: definitional distinctions and educational gaps (2026) · cites this paper as [70]
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Metadata
- Authors
- Ybarra, M. L., Boyd, D., Korchmaros, J. D., & Oppenheim, J. (.
- Affiliations
- Center for Innovative Public Health Research (US)
- Microsoft (United States) (US)
- The Graduate Center, CUNY (US)
- Venue
- Journal of Adolescent Health
- Publisher
- Elsevier BV
- Published
- 2012-07
- Volume
- 51
- Issue
- 1
- Pages
- 53-58
- ISSN
- 1054-139X (ISSN-L)
- PMC
- PMC3383604
- OpenAlex
- W2021243193
- Cited by
- 296
- References
- 27
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 13 · 2023: 13 · 2022: 17 · 2021: 24 · 2020: 15 · 2019: 25 · 2018: 28 · 2017: 26 · 2016: 25 · 2015: 36 · 2014: 32 · 2013: 19 · 2012: 7
- FWCI
- 45.36
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of Adolescent Health (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD057191)
- Topics
- Social Sciences › Psychology › Social Psychology › Bullying, Victimization, and Aggression
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Workplace Violence and Bullying
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- cyberbullying, bullying victimization, bullying measurement, definition of bullying, prevalence rates, misclassification, repetition, text messaging, adolescents, school bullying
- MeSH terms
- Adolescent, Child, Data Collection, Female, Humans, Male, Terminology as Topic, United States, Reproducibility of Results, Prevalence, Crime Victims, Internet, Bullying
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