Cited by 19Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningSpam and Phishing Detection

Detecting Kids Cyberbullying Using Transfer Learning Approach: Transformer Fine-Tuning Models

Yafooz, Wael M. S., Al-Dhaqm, Arafat, Alsaeedi, Abdullah·2023·Studies in Computational Intelligence

Integrity signals

Registry

Crossref · 10.1007/978-3-031-21199-7_18

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?

Cites

Flagged first, then by citations

1 of the 36 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Yafooz, W. M. S., Al-Dhaqm, A., & Alsaeedi, A.
Affiliations
Taibah University (SA)
Corresponding author
Yafooz, Wael M. S.
Venue
Studies in Computational Intelligence
Published
2023
Pages
255-267
ISSN
  • 1860-949X (ISSN-L)
  • 1860-9503
OpenAlex
W4321442855
Cited by
17
References
36
Citations by year
2026: 1 · 2025: 4 · 2024: 4 · 2023: 8
FWCI
9.41
Type
book chapter
Language
en
Open access
closed
Locations
Studies in computational intelligence (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Keywords
cyberbullying detection, children, transfer learning, transformer fine-tuning, BERT, social media comments, digital platforms, inappropriate content, text classification

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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