The DOI matches its Crossref record.
Hatespeechbert: Retraining Bert for Automatic Hate Speechdetection
Open accessOverview
Integrity signals
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Cites
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Claims the paper makes
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?
Ishaq et al. demonstrated that HSBERT provides state-of-the-art results compared to general-domain BERT.
“They demonstrate in their study that HSBERT provides state-of-the-art results when compared to other models by comparing its performance against the general-domain BERT through extrinsic and intrinsic evaluations.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [62]
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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
Metadata
- Authors
- Ishaq, A., Malik, K. M., & Zafar, A.
- Affiliations
- Riphah International University (PK)
- Oakland University (US)
- Corresponding author
- Malik, Khalid Mahmood
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2023
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4320017350
- Cited by
- 1
- References
- 0
- Citations by year
- 2023: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- 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 › Topic Modeling
- Keywords
- hate speech detection, BERT, domain-specific pre-training, Hugging Face, social media, online harassment, pre-trained language models
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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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