A multi-platform dataset for detecting cyberbullying in social media
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A small volume of data was used to train models for the classification of both cyberbullying and non-cyberbullying text.
“Based on the utilization of a small dataset for training learning models to detect cyberbullying on social media platforms, the review identified that a small volume of data was used to train models for the classification of both cyberbullying and non-cyberbullying text [147,148].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [148]
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- Authors
- Van Bruwaene, D., Huang, Q., & Inkpen, D.
- Affiliations
- University of Ottawa (CA)
- Venue
- Language Resources and Evaluation
- Published
- 2020-04-06
- Volume
- 54
- Issue
- 4
- Pages
- 851-874
- ISSN
- 1574-020X (ISSN-L)
- 1574-0218
- OpenAlex
- W3015536985
- Cited by
- 74
- References
- 39
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 11 · 2023: 10 · 2022: 17 · 2021: 14 · 2020: 4
- FWCI
- 4.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Language Resources and Evaluation (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada (570515-181099)
- Ontario Centres of Excellence (24543)
- VISR Inc.
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- cyberbullying detection, social media, annotation system, crowdsourcing, post-selection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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