Cited by 89Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningText and Document Classification Technologies

BERT-based ensemble learning for multi-aspect hate speech detection

Mazari, Ahmed Cherif, Boudoukhani, Nesrine, Djeffal, Abdelhamid·2024·Cluster Computing

Integrity signals

Registry

Crossref · 10.1007/s10586-022-03956-x

Cites

41 of 41 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?

  • BERT has produced outstanding results on natural language understanding tasks, and in numerous studies it outperformed conventional machine learning algorithms and demonstrated promising results in cyberbullying detection.

    “On a variety of natural language understanding tasks, Bidirectional Encoder Representation from Transformers (BERT) has produced outstanding results. In numerous studies, BERT outperformed conventional machine learning algorithms, achieved cutting-edge performance, and demonstrated promising results in the detection of cyberbullying.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [58]

  • Studies show BERT achieved high accuracy and F1 scores to classify cyberbullying in various types of online content, such as tweets and comments.

    “Studies [22,58-60] show that BERT achieved high accuracy and F1 scores to classify cyberbullying in various types of online content, such as tweets and comments.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [58]

Cites

Flagged first, then by citations

3 of the 44 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Mazari, A. C., Boudoukhani, N., & Djeffal, A.
Affiliations
  • University Yahia Fares of Medea (DZ)
  • Université Djilali de Sidi Bel Abbès (DZ)
  • University of Biskra (DZ)
Corresponding author
Mazari, Ahmed Cherif
Venue
Cluster Computing
Published
2023-01-03
Volume
27
Issue
1
Pages
325-339
ISSN
  • 1386-7857 (ISSN-L)
  • 1573-7543
OpenAlex
W4313531555
Cited by
89
References
44
Citations by year
2026: 23 · 2025: 29 · 2024: 25 · 2023: 12
FWCI
8.39
Type
journal article
Language
en
Open access
closed
Locations
Cluster Computing (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 › Artificial Intelligence › Text and Document Classification Technologies
Keywords
multi-label text classification, BERT, ensemble learning, BiLSTM, BiGRU, GloVe embeddings, FastText embeddings, toxic language, social media toxicity, cyberbullying

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 ›

Are you an author, or do you see a problem with this page? Request a review