The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Abusive Language Detection in Online User Content
Abstract
Detection of abusive language in user generated online content has become an issue of increasing importance in recent years. Most current commercial methods make use of blacklists and regular expressions, however these measures fall short when contending with more subtle, less ham-fisted examples of hate speech. In this work, we develop a machine learning based method to detect hate speech on online user comments from two domains which outperforms a state-of-the-art deep learning approach. We also develop a corpus of user comments annotated for abusive language, the first of its kind. Finally, we use our detection tool to analyze abusive language over time and in different settings to further enhance our knowledge of this behavior.
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Cites
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Cited by
Automated Hate Speech Detection and the Problem of Offensive Language
Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14955 · Cited by 2,610
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
Ex Machina: Personal Attacks Seen at Scale
Wulczyn, E., Thain, N., & Dixon, L. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052591 · Cited by 636
Understanding Abuse: A Typology of Abusive Language Detection Subtasks
Waseem, Z., Davidson, T., Warmsley, D., & Weber, I. (2017) · Proceedings of the First Workshop on Abusive Language Online · doi:10.18653/v1/w17-3012 · Cited by 474
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media
Mozafari, M., Farahbakhsh, R., & Crespi, N. (2020) · Studies in Computational Intelligence · doi:10.1007/978-3-030-36687-2_77 · Cited by 396
Metadata
- Authors
- Nobata, C., Tetreault, J., Thomas, A., Mehdad, Y., & Chang, Y.
- Venue
- Proceedings of the 25th International Conference on World Wide Web
- Published
- 2016-04-11
- Pages
- 145-153
- OpenAlex
- W2340954483
- Cited by
- 1,122
- References
- 25
- FWCI
- 58.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Nobata, C., Tetreault, J., Thomas, A., Mehdad, Y., & Chang, Y. (2016). Abusive Language Detection in Online User Content. Proceedings of the 25th International Conference on World Wide Web. https://doi.org/10.1145/2872427.2883062
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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