Cited by 4Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningMisinformation and Its Impacts

Utilising Twitter Metadata for Hate Classification

Warke, Oliver, Jose, Joemon M., Breitsohl, Jan·2023·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/978-3-031-28238-6_59

Cites

25 of 25 references verified0 citation errors · 0 not checkable

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  • Several studies used DistilBERT for cyberbullying detection in social networks and obtained promising performance.

    “Several studies [54-56] used DistilBERT for cyberbullying detection in social networks. Their experimental results show that they obtained a promising performance while using DistilBERT as the word-embedding technique as well as the fine-tuned classifier.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [56]

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Authors
Warke, O., Jose, J. M., & Breitsohl, J.
Affiliations
University of Glasgow (GB)
Corresponding author
Warke, Oliver
Venue
Lecture Notes in Computer Science
Published
2023
Pages
676-684
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W4327644089
Cited by
4
References
25
Citations by year
2025: 1 · 2024: 2 · 2023: 1
FWCI
0.71
Type
conference paper
Language
en
Open access
closed
Locations
Lecture notes in computer science (publishedVersion)
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
hate speech detection, BERT classifier, social media abuse, text classification

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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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