Detecting context abusiveness using hierarchical deep learning

Ju-Hyoung Lee, Jun-U Park, Jeong-Won Cha, Yo-Sub Han


Abstract
Abusive text is a serious problem in social media and causes many issues among users as the number of users and the content volume increase. There are several attempts for detecting or preventing abusive text effectively. One simple yet effective approach is to use an abusive lexicon and determine the existence of an abusive word in text. This approach works well even when an abusive word is obfuscated. On the other hand, it is still a challenging problem to determine abusiveness in a text having no explicit abusive words. Especially, it is hard to identify sarcasm or offensiveness in context without any abusive words. We tackle this problem using an ensemble deep learning model. Our model consists of two parts of extracting local features and global features, which are crucial for identifying implicit abusiveness in context level. We evaluate our model using three benchmark data. Our model outperforms all the previous models for detecting abusiveness in a text data without abusive words. Furthermore, we combine our model and an abusive lexicon method. The experimental results show that our model has at least 4% better performance compared with the previous approaches for identifying text abusiveness in case of with/without abusive words.
Anthology ID:
D19-5002
Volume:
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Anna Feldman, Giovanni Da San Martino, Alberto Barrón-Cedeño, Chris Brew, Chris Leberknight, Preslav Nakov
Venue:
NLP4IF
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10–19
Language:
URL:
https://aclanthology.org/D19-5002
DOI:
10.18653/v1/D19-5002
Bibkey:
Cite (ACL):
Ju-Hyoung Lee, Jun-U Park, Jeong-Won Cha, and Yo-Sub Han. 2019. Detecting context abusiveness using hierarchical deep learning. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 10–19, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Detecting context abusiveness using hierarchical deep learning (Lee et al., NLP4IF 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/fix-dup-bibkey/D19-5002.pdf