Thomas Mandl
Sandip Modha
ABSTRACT
The identification of Hate Speech in Social Media is of great importance and receives much attention in the text classification community. There is a huge demand for research for languages other than English. The HASOC track intends to stimulate development in Hate Speech for Hindi, German and English. Three datasets were developed from Twitter and Facebook and made available. Binary classification and more fine-grained subclasses were offered in 3 subtasks. For all subtasks, 321 experiments were submitted. The approaches used most often were LSTM networks processing word embedding input. The performance of the best system for identification of Hate Speech for English, Hindi, and German was a Marco-F1 score of 0.78, 0.81 and 0.61, respectively.
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Digital Library
- Schmidt, A., & Wiegand, M. (2017, April). A survey on hate speech detection using natural language processing. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media (pp. 1--10).Google ScholarCross Ref
- Wiegand, M., Siegel, M., & Ruppenhofer, J. (2018). Overview of the Germeval 2018 shared task on the identification of offensive language. Proceedings of GermEval 2018, https://ids-pub.bsz-bw.de/files/8493/Wiegand_Siegel_Ruppenhofer_Overview_of_the_GermEval_2018.pdfGoogle Scholar
- Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., & Kumar, R. (2019). Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval). arXiv preprint arXiv:1903.08983.Google Scholar
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