Cited by 42Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningSpam and Phishing Detection

Performance analysis of transformer-based architectures and their ensembles to detect trait-based cyberbullying

Ahmed, Tasnim, Ivan, Shahriar, Kabir, Mohsinul, Mahmud, Hasan, Hasan, Kamrul·2022·Social Network Analysis and Mining

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Registry

Crossref · 10.1007/s13278-022-00934-4

Cites

37 of 37 references verified0 citation errors · 0 not checkable

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Claims the paper makes

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

  • Transformers are deep learning models based on attention that have shown effectiveness in detecting cyberbullying.

    “Transformers are deep learning models based on attention that have shown effectiveness in detecting cyberbullying [113-115].”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [114]

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  • A Review on Deep-Learning-Based Cyberbullying Detection

    Hasan, M. T., Hossain, M. A. E., Mukta, M. S. H., Akter, A., Ahmed, M., & Islam, S. (2023) · Future Internet · doi:10.3390/fi15050179 · Cited by 103 · see citing works

    “…textual bullies [148], Instagram bullied text identification [118], Abusive Bangla Comment detection [121], Trait-based bullying detection [114] The method provides a simple and efficient architecture with a fixed length vector to pay attention of a sentence’s high-level meaning The model…”
    Method

Authors
Ahmed, T., Ivan, S., Kabir, M., Mahmud, H., & Hasan, K.
Affiliations
Islamic University of Technology (BD)
Corresponding author
Ahmed, Tasnim
Venue
Social Network Analysis and Mining
Published
2022-08-02
Volume
12
Issue
1
ISSN
  • 1869-5450 (ISSN-L)
  • 1869-5469
OpenAlex
W4289523473
Cited by
42
References
37
Citations by year
2026: 10 · 2025: 11 · 2024: 12 · 2023: 5 · 2022: 4
FWCI
2.50
Type
journal article
Language
en
Open access
closed
Locations
Social Network Analysis and Mining (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, transformer models, ensemble learning, social media comments, Twitter, class imbalance, automated content moderation

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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.

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