Abstract
Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 F-measure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps.- Anthology ID:
- W17-3006
- Volume:
- Proceedings of the First Workshop on Abusive Language Online
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, BC, Canada
- Editors:
- Zeerak Waseem, Wendy Hui Kyong Chung, Dirk Hovy, Joel Tetreault
- Venue:
- ALW
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 41–45
- Language:
- URL:
- https://aclanthology.org/W17-3006
- DOI:
- 10.18653/v1/W17-3006
- Cite (ACL):
- Ji Ho Park and Pascale Fung. 2017. One-step and Two-step Classification for Abusive Language Detection on Twitter. In Proceedings of the First Workshop on Abusive Language Online, pages 41–45, Vancouver, BC, Canada. Association for Computational Linguistics.
- Cite (Informal):
- One-step and Two-step Classification for Abusive Language Detection on Twitter (Park & Fung, ALW 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/W17-3006.pdf