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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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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
IOP Conference Series: Materials Science and Engineering
(2022) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1231/1/011001 · Cited by 12,131
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,869
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645
Natural Language Processing (almost) from ScratchPreprint
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,923
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878
Bag of Tricks for Efficient Text Classification
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers · doi:10.18653/v1/e17-2068 · Cited by 4,545
Learning deep structured semantic models for web search using clickthrough data
Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., & Heck, L. P. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505665 · Cited by 2,120
Bullying, Cyberbullying, and Suicide
Hinduja, S., & Patchin, J. W. (2010) · Archives of Suicide Research · doi:10.1080/13811118.2010.494133 · Cited by 2,078
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
Abualsaud, M., & Smucker, M. (2019) · doi:10.1145/3357384 · Cited by 1,299
Abusive Language Detection in Online User Content
Nobata, C., Tetreault, J., Thomas, A. O., Mehdad, Y., & Chang, Y. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883062 · Cited by 1,197
Proceedings of the 24th International Conference on World Wide Web
Aldo Gangemi, A., Leonardi, S., & Panconesi, A. (2015) · doi:10.1145/2740908 · Cited by 1,080
SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., & Kumar, R. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2010 · Cited by 816
A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
Shen, Y., He, X., Gao, J., Deng, L., & Mesnil, G. (2014) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2661829.2661935 · Cited by 686
Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model
Wang, J., Yu, L.-C., Lai, K.-H. R., & Zhang, X. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2037 · Cited by 618
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior
Founta, A.-M., Djouvas, C., Chatzakou, D., Ilias Leontiadis, A., Blackburn, J., Stringhini, G., Vakali, A. I., Sirivianos, M., & Kourtellis, N. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.14991 · Cited by 605
Hate speech detection: Challenges and solutions
MacAvaney, S., Yao, H.-R., Yang, E., Russell, K., Goharian, N., & Frieder, O. (2019) · PLOS ONE · doi:10.1371/journal.pone.0221152 · Cited by 566
Using Convolutional Neural Networks to Classify Hate-Speech
Gambäck, B., & Sikdar, U. K. (2017) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w17-3013 · Cited by 561
Proceedings of the 18th international conference on World wide web
(2009) · doi:10.1145/1526709 · Cited by 507
Understanding Abuse: A Typology of Abusive Language Detection Subtasks
Waseem, Z., Davidson, T., Warmsley, D., & Weber, I. G. (2017) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w17-3012 · Cited by 486
Us and them: identifying cyber hate on Twitter across multiple protected characteristics
Burnap, P., & Williams, M. L. (2016) · EPJ Data Science · doi:10.1140/epjds/s13688-016-0072-6 · Cited by 397
Hate speech detection: A solved problem? The challenging case of long tail on Twitter
Zhang, Z., & Luo, L. (2018) · Semantic Web · doi:10.3233/sw-180338 · Cited by 372
Proceedings of the 22nd ACM international conference on Information & Knowledge Management
He, Q., Iyengar, A., Nejdl, W., Pei, J., & Rastogi, R. R. (2013) · Cited by 281
A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration
Qin, D., Yu, J. X., Zou, G., Yong, R., Zhao, Q., & Zhang, B. (2019) · IEEE Access · doi:10.1109/access.2019.2897028 · Cited by 273
Barzilay, R., & Kan, M.-Y. (2017) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p17-2 · Cited by 220
Challenges of Hate Speech Detection in Social Media
Kovács, G., Alonso, P., & Saini, R. (2021) · SN Computer Science · doi:10.1007/s42979-021-00457-3 · Cited by 162
Purely URL-based topic classification
Baykan, E., Henzinger, M. R., Marian, L., & Weber, I. G. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526880 · Cited by 121
Oriola, O., & Kotzé, E. (2020) · IEEE Access · doi:10.1109/access.2020.2968173 · Cited by 111
BERT and fastText Embeddings for Automatic Detection of Toxic Speech
d'Sa, A. G., Illina, I., & Fohr, D. (2020) · International Multi-Conference on Organization of Knowledge and Advanced Technologies (OCTA) · doi:10.1109/octa49274.2020.9151853 · Cited by 102
Deep learning for detecting inappropriate content in text
Yenala, H., Jhanwar, A., Chinnakotla, M. K., & Goyal, J. (2017) · International Journal of Data Science and Analytics · doi:10.1007/s41060-017-0088-4 · Cited by 99
Combining link-based and content-based methods for web document classification
Calado, P. P., de Cristo, M. A. P., de Moura, E. S., Ziviani, N., Ribeiro-Neto, B. A., & Gonçalves, M. A. (2003) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/956863.956938 · Cited by 98
García-Díaz, J. A., Jiménez-Zafra, S. M., Cumbreras, M. Á. G., & Valencia‐García, R. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00693-x · Cited by 78
Kumari, K., Singh, J. P., Dwivedi, Y. K., & Rana, N. P. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.01.014 · Cited by 74
Proceedings of the First Workshop on Abusive Language Online
(2017) · doi:10.18653/v1/w17-30 · Cited by 63
Fišer, D., Erjavec, T., & Ljubešić, N. (2017) · doi:10.18653/v1/w17-3007 · Cited by 56
Modeling Annotator Perspective and Polarized Opinions to Improve Hate Speech Detection
Akhtar, S., Basile, V., & Patti, V. (2020) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v8i1.7473 · Cited by 55
Ostracizing targets of workplace sexual harassment before and after the #MeToo movement
Brown, S. E. V., & Battle, J. S. (2019) · Equality Diversity and Inclusion An International Journal · doi:10.1108/edi-09-2018-0162 · Cited by 51
Hate Speech Detection Using Static BERT Embeddings
Rajput, G. K., Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-93620-4_6 · Cited by 36
A Hierarchical multi-input and output Bi-GRU Model for Sentiment Analysis on Customer Reviews
Zhang, L., Zhou, Y., Duan, X., & Chen, R. (2018) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/322/6/062007 · Cited by 29
Comparison of Pretrained Embeddings to Identify Hate Speech in Indian Code-Mixed Text
Banerjee, S., Chakravarthi, B. R., & McCrae, J. P. (2020) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icacccn51052.2020.9362731 · Cited by 27
2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA)
Krichen, S., Ben-Romdhane, H., & Sidhom, S. (2020) · doi:10.1109/octa49274.2020 · Cited by 13
Advances in Data and Information Sciences
Tiwari, S., Trivedi, M. C., Kolhe, M. L., & Singh, B. K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-5292-0 · Cited by 10
Research works printed but not linked: 11
- Gaydhani, A., Doma, V., Kendre, S., Bhagwat, L., 2018. Detecting hate speech and offensive language on twitter using machine learning: An n-gram and tfdf based approach. arXiv preprint arXiv:1809.08651 . · arXiv
- Giglou, 2021, Profling haters on twitter using statistical and contextualized embeddings, CLEF
- Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V., 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 . · arXiv
- Mollas, I., Chrysopoulou, Z., Karlos, S., Tsoumakas, G., 2020. Ethos: an online hate speech detection dataset. arXiv preprint arXiv:2006.08328. · arXiv
- Pitsilis, G.K., Ramampiaro, H., Langseth, H., 2018. Detecting ofensive language in tweets using deep learning. arXiv preprint arXiv:1801.04433 . · arXiv
- Ramraj, 2016, Experimenting xgboost algorithm for prediction and classification of different datasets, International Journal of Control Theory and Applications, 9, 651
- Romaissa Beddiar, D., Saroar Jahan, M., Oussalah, M., 2021. Data expansion using back translation and paraphrasing for hate speech detection. arXiv e-prints, arXiv–2106. · DOI
- Saha, P., Mathew, B., Goyal, P., Mukherjee, A., 2019. Hatemonitors: Language agnostic abuse detection in social media. arXiv preprint arXiv:1909.12642 . · arXiv
- Sanh, V., Debut, L., Chaumond, J., Wolf, T., 2019. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 . · arXiv
- Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V., 2019. Xlnet: Generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237 . · arXiv
- Young, 2018, 13, 55
Cited by
Sorted by citations
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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
Albladi, A., Islam, M., Das, A., Bigonah, M., Zhang, Z., Jamshidi, F., Rahgouy, M., Raychawdhary, N., Marghitu, D., & Seals, C. (2025) · IEEE Access · doi:10.1109/access.2025.3532397 · Cited by 72
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
Jafari, A. R., Li, G., Rajapaksha, P., Farahbakhsh, R., & Crespi, N. (2023) · IEEE Access · doi:10.1109/access.2023.3318863 · Cited by 26
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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