The Effects of User Features on Twitter Hate Speech Detection

Elise Fehn Unsvåg, Björn Gambäck


Abstract
The paper investigates the potential effects user features have on hate speech classification. A quantitative analysis of Twitter data was conducted to better understand user characteristics, but no correlations were found between hateful text and the characteristics of the users who had posted it. However, experiments with a hate speech classifier based on datasets from three different languages showed that combining certain user features with textual features gave slight improvements of classification performance. While the incorporation of user features resulted in varying impact on performance for the different datasets used, user network-related features provided the most consistent improvements.
Anthology ID:
W18-5110
Volume:
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
ALW | EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
75–85
Language:
URL:
https://aclanthology.org/W18-5110
DOI:
10.18653/v1/W18-5110
Bibkey:
Cite (ACL):
Elise Fehn Unsvåg and Björn Gambäck. 2018. The Effects of User Features on Twitter Hate Speech Detection. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 75–85, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
The Effects of User Features on Twitter Hate Speech Detection (Fehn Unsvåg & Gambäck, 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5110.pdf