HARALD: Augmenting Hate Speech Data Sets with Real Data

Tal Ilan, Dan Vilenchik

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

The successful completion of the hate speech detection task hinges upon the availability of rich and variable labeled data, which is hard to obtain. In this work, we present a new approach for data augmentation that uses as input real unlabelled data, which is carefully selected from online platforms where invited hate speech is abundant. We show that by harvesting and processing this data (in an automatic manner), one can augment existing manually-labeled datasets to improve the classification performance of hate speech classification models. We observed an improvement in F1-score ranging from 2.7% and up to 9.5%, depending on the task (in- or cross-domain) and the model used.

Original languageEnglish
Pages2241-2248
Number of pages8
StatePublished - 1 Jan 2022
Event2022 Findings of the Association for Computational Linguistics: EMNLP 2022 - Abu Dhabi, United Arab Emirates
Duration: 7 Dec 202211 Dec 2022

Conference

Conference2022 Findings of the Association for Computational Linguistics: EMNLP 2022
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period7/12/2211/12/22

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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