Quantifying the impact of context on the quality of manual hate speech annotation. Natural Language Engineering
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Description
The quality of annotations in manually annotated hate speech datasets is crucial for automatic hate speech detection. This contribution focuses on the positive effects of manually annotating online comments for...
show moreNikola Ljubešić Jožef Stefan Institute, Ljubljana, Slovenia Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
Igor Mozetič Jožef Stefan Institute, Ljubljana, Slovenia
Petra Kralj Novak Jožef Stefan Institute, Ljubljana, Slovenia Central European University, Vienna, Austria
Corresponding author. Nikola Ljubešić E-mail: nikola.ljubesic@ijs.si
This is an Open Access article, distributed under the terms of the Creative Commons Attribution license, which permits unrestricted re use, distribution, and reproduction in any medium, provided the original work is properly cited.
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This was produced by Brandon Casturo
Ljubešić, N., Mozetič, I., & Kralj Novak, P. (2022). Quantifying the impact of context on the quality of manual hate speech annotation. Natural Language Engineering, 1-14. doi:10.1017/S1351324922000353
https://www.cambridge.org/core/journals/natural-language-engineering/article/quantifying-the-impact-of-context-on-the-quality-of-manual-hate-speech-annotation/B6E813E528CE094DBE489ABD3A047D8A
Hate speech
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