Cited by 74Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningMisinformation and Its Impacts

A multi-platform dataset for detecting cyberbullying in social media

Van Bruwaene, David, Huang, Qianjia, Inkpen, Diana·2020·Language Resources and Evaluation

Integrity signals

Registry

Crossref · 10.1007/s10579-020-09488-3

Cites

35 of 35 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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].”

Cites

Flagged first, then by citations

4 of the 39 references couldn’t be matched to a known paper.

Cited by

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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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