Global research trends on cyberbullying: A bibliometric study
Bibliographic record
Abstract
The rapid growth of the media industry, particularly social media, has enhanced interaction and information sharing but has also led to harmful uses of cyberspace, such as cyberbullying. This phenomenon, primarily affecting adolescents, involves repeated harm through electronic devices in forms like abusive or aggressive text messages, inappropriate videos, and identity theft. The present study utilizes the Scopus database to analyze 5201 publications on cyberbullying from 1999 to 2023. Using various bibliometric network methods for analysis such as networks, citation, co-citation, collaboration, and keyword co-occurrence networks, along with intellectual structure maps, we identified key contributors and publications from this field. The study identifies significant growth in scientific output over the years, with prominent contributors like Michelle F. Wright, Heidi Vandebosch, and Rosario Ortega-Ruiz, and key journals including Computers in Human behavior , International Journal of Environmental Research and Public Health, and Journal of Interpersonal Violence. The United States leads research production, with substantial collaboration among American institutions, followed by Canada and the United Kingdom. This study recognizes social media, gender, and online abuse as key topics well-explored in studies on cyberbullying. However, further investigation is required in fields such as cyber dating violence and harassment, along with the associated challenges faced by sexual minorities. Our results show a growing research interest among academics in understanding the various aspects of cyberbullying in recent years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.162 | 0.305 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".