Transformer-based models for hate speech classification Article Swipe
Deepti Jain
,
Sandhya Arora
,
C. K. Jha
,
Garima Malik
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0198822
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0198822
This research paper explores the application of text classification and natural language processing techniques for enhancing hate speech detection. The study employs machine learning (ML) and deep learning models, including transformer models such as BERT, RoBERTa, and DistilBERT, to improve the accuracy of hate speech classifiers. Through a comprehensive empirical analysis on three diverse datasets (Data-ICWSM, Data-ALW2, Data-OLID), the study demonstrates the effectiveness of these models in accurately identifying hate speech. Compared to traditional baselines, the BERT models exhibit a significant performance boost in macro and weighted F1-scores. Additionally, the study addresses the challenge of imbalanced class distributions in the datasets by employing sampling techniques during training. Overall, the research highlights the potential of transformer models for hate speech detection and provides insights for future exploration in this domain.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0198822
- https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0198822/19833345/020017_1_5.0198822.pdf
- OA Status
- bronze
- Cited By
- 1
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392947462
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392947462Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1063/5.0198822Digital Object Identifier
- Title
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Transformer-based models for hate speech classificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
-
Deepti Jain, Sandhya Arora, C. K. Jha, Garima MalikList of authors in order
- Landing page
-
https://doi.org/10.1063/5.0198822Publisher landing page
- PDF URL
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https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0198822/19833345/020017_1_5.0198822.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0198822/19833345/020017_1_5.0198822.pdfDirect OA link when available
- Concepts
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Computer science, Transformer, Speech recognition, Artificial intelligence, Natural language processing, Engineering, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(Data-ICWSM, | 55 |
| abstract_inverted_index.classifiers. | 45 |
| abstract_inverted_index.demonstrates | 60 |
| abstract_inverted_index.Additionally, | 88 |
| abstract_inverted_index.comprehensive | 48 |
| abstract_inverted_index.distributions | 97 |
| abstract_inverted_index.effectiveness | 62 |
| abstract_inverted_index.classification | 8 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/5 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Gender equality |
| citation_normalized_percentile.value | 0.65485184 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |