One-step and Two-step Classification for Abusive Language Detection on Twitter

Ji Ho Park, Pascale Fung


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
Bibkey:
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)
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PDF:
https://aclanthology.org/W17-3006.pdf