Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter

Zeerak Waseem, Dirk Hovy


Abstract
Hate speech in the form of racist and sexist remarks are a common occurrence on social media. For that reason, many social media services address the problem of identifying hate speech, but the definition of hate speech varies markedly and is largely a manual effort (BBC, 2015; Lomas, 2015). We provide a list of criteria founded in critical race theory, and use them to annotate a publicly available corpus of more than 16k tweets. We analyze the impact of various extra-linguistic features in conjunction with character n-grams for hate-speech detection. We also present a dictionary based the most indicative words in our data.
Anthology ID:
N16-2013
Volume:
Proceedings of the NAACL Student Research Workshop
Month:
June
Year:
2016
Address:
San Diego, California
Editors:
Jacob Andreas, Eunsol Choi, Angeliki Lazaridou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
88–93
Language:
URL:
https://preview.aclanthology.org/ingest-brigap/N16-2013/
DOI:
10.18653/v1/N16-2013
Award:
 ACL 2026 Test of Time Award
Bibkey:
Cite (ACL):
Zeerak Waseem and Dirk Hovy. 2016. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. In Proceedings of the NAACL Student Research Workshop, pages 88–93, San Diego, California. Association for Computational Linguistics.
Cite (Informal):
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter (Waseem & Hovy, NAACL 2016)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-brigap/N16-2013.pdf