Cyberbullying definitions and measurements in children and adolescents: Summarizing 20 years of global efforts
Bibliographic record
Abstract
Despite numerous instruments existing to assist in the measurement of specific cyberbullying behaviors or cyberbullying in general, it is still unclear their purpose, corresponding scenarios, and their effectiveness. This study, therefore, aims to provide a comprehensive review of academic efforts on cyberbullying definitions, measurements, and their effectiveness in children and adolescents in the past two decades. A systematic review was performed using ASReview, an open source machine learning systematic review system. Three bibliographic citation databases, including Web of Science core collection, PubMed, and EBSCO were adopted for all relevant literature published from January 2001 to August 2021. In total, twenty-five studies, mentioning seventeen cyberbullying measurement scales, met the study collection criteria. The results found that most failed to provide a clear definition of cyberbullying, often providing unclear and inconsistent descriptions for the youth. Similarly, studies found it difficult to clearly reflect the three key elements of bullying, namely: harmfulness, repetitiveness, and the power imbalance between bullies and victims. With regard to cyberbullying types, most presented two or three categories, including victimization, perpetration, and bystanding, while some suggested four types based on the nature of the cyberbullying behavior, including written or verbal, visual or sexual, character impersonation, and exclusion. If characteristics are considered, cyberbullying becomes more specific with multiple categories being proposed, including flaming (or roasting), harassment, denigration, defamation, outing, jokes, online sexual harassment, and cyberstalking. With regard to measurements, many scales have been proposed and frequently refined to capture specific cyberbullying experience of the youth. This study emphasizes the value and importance of providing clear cyberbullying definitions and helps scholars in youth cyberbullying choose appropriate measurement scales.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".