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Locate the Hate: Detecting Tweets against Blacks
Open accessAbstract
Although the social medium Twitter grants users freedom of speech, its instantaneous nature and retweeting features also amplify hate speech. Because Twitter has a sizeable black constituency, racist tweets against blacks are especially detrimental in the Twitter community, though this effect may not be obvious against a backdrop of half a billion tweets a day.1 We apply a supervised machine learning approach, employing inexpensively acquired labeled data from diverse Twitter accounts to learn a binary classifier for the labels “racist” and “nonracist.” The classifier has a 76% average accuracy on individual tweets, suggesting that with further improvements, our work can contribute data on the sources of anti-black hate speech.
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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
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
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
Metadata
- Authors
- Kwok, I., & Wang, Y.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2013-06-29
- Volume
- 27
- Issue
- 1
- Pages
- 1621-1622
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W80056832
- Cited by
- 446
- References
- 0
- FWCI
- 2.38
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Kwok, I., & Wang, Y. (2013). Locate the Hate: Detecting Tweets against Blacks. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v27i1.8539
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