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Combining FastText and Glove Word Embedding for Offensive and Hate speech Text Detection
Open accessAbstract
Over the past decade, increased use of social media has led to an increase in hate content. To address this issue new solutions must be implemented to filter out this kind of inappropriate content. Because manual filtering is difficult, several studies have been conducted in order to automate the process. This paper introduces a method based on a combination of Glove and FastText word embedding as input features and a BiGRU model to identify hate speech from social media websites. The obtained results show that our proposed model (BiGRU Glove FT) is effective in detecting inappropriate content. This model detect hate speech on OLID dataset, using an effective learning process that classifies the text into offensive and not offensive language. The performance of the system attained 84%, 87%, 93%, 90% accuracy, precision, recall, and f1-score respectively.
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“The GloVe algorithm utilizes word co-occurrence data or global statistics to infer semantic relationships between words in the corpus, as opposed to word2vec's dependence on local context windows for deriving these associations (Badri et al. 2022; Gan et al. 2022; Curto et al. 2022).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Badri et al. 2022
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“Pre-trained word embeddings can predict word contexts in an unsupervised manner, assuming that words in close proximity within a sentence have similar meanings (Badri et al. 2022; Kowsher et al. 2022).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Badri et al. 2022
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Cited by
Sorted by citations
Large language models (LLMs): survey, technical frameworks, and future challenges
Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395
Li, L., Fan, L., Atreja, S., & Hemphill, L. (2024) · ACM Transactions on the Web · doi:10.1145/3643829 · Cited by 139
Hate Speech Detection Using Large Language Models: A Comprehensive Review
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Contextual Embeddings-Based Web Page Categorization Using the Fine-Tune BERT Model
Nandanwar, A. K., & Choudhary, J. (2023) · Symmetry · doi:10.3390/sym15020395 · Cited by 30
Fine-Grained Emotions Influence on Implicit Hate Speech Detection
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Metadata
- Authors
- Badri, N., Kboubi, F., & Chaibi, A. H.
- Affiliations
- National School of Computer Science (TN)
- Corresponding author
- Badri, Nabil
- ORCID
- Badri, Nabil
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 207
- Pages
- 769-778
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W4312750750
- Cited by
- 65
- References
- 57
- Citations by year
- 2026: 17 · 2025: 14 · 2024: 16 · 2023: 17 · 2022: 1
- FWCI
- 6.63
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- 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
- hate speech detection, offensive language detection, GloVe embeddings, FastText word embeddings, bidirectional GRU
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