The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A Survey on Hate Speech Detection using Natural Language Processing
Open accessAbstract
This paper presents a survey on hate speech detection.Given the steadily growing body of social media content, the amount of online hate speech is also increasing.Due to the massive scale of the web, methods that automatically detect hate speech are required.Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing.We also discuss limits of those approaches.
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Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
(1992) · doi:10.5555/176313.176316 · Cited by 2,892
Sentiment strength detection in short informal text
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21416 · Cited by 1,449
Detection of Cyberbullying Incidents on the Instagram Social Network
Hosseinmardi, H., Mattson, S. A., Rafiq, R. I., Han, R., Lv, Q., & Mishra, S. (2015) · arXiv · doi:10.48550/arxiv.1503.03909 · Cited by 126
Cited by
A Survey on Automatic Detection of Hate Speech in Text
Fortuna, P., & Nunes, S. (2019) · ACM Computing Surveys · doi:10.1145/3232676 · Cited by 1,241
The Risk of Racial Bias in Hate Speech Detection
Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1163 · Cited by 817
The design and implementation of Xiaoice, an empathetic social chatbot
Zhou, L., Gao, J., Li, D., & Shum, H.-Y. (2020) · Computational Linguistics · doi:10.1162/coli_a_00368 · Cited by 777
Ethical and social risks of harm from Language Models
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.04359 · Cited by 477
Racial Bias in Hate Speech and Abusive Language Detection Datasets
Davidson, T., Bhattacharya, D., & Weber, I. (2019) · Proceedings of the Third Workshop on Abusive Language Online · doi:10.18653/v1/w19-3504 · Cited by 414
Metadata
- Authors
- Schmidt, A., & Wiegand, M.
- Venue
- Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media
- Published
- 2017
- Pages
- 1-10
- OpenAlex
- W2740168486
- Cited by
- 1,395
- References
- 4
- FWCI
- 54.62
- Type
- conference paper
- Language
- en
- Open access
- gold
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