Detecting Hate Speech in Social Media

Shervin Malmasi, Marcos Zampieri


Abstract
In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by applying supervised classification methods using a recently released dataset annotated for this purpose. As features, our system uses character n-grams, word n-grams and word skip-grams. We obtain results of 78% accuracy in identifying posts across three classes. Results demonstrate that the main challenge lies in discriminating profanity and hate speech from each other. A number of directions for future work are discussed.
Anthology ID:
R17-1062
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
467–472
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_062
DOI:
10.26615/978-954-452-049-6_062
Bibkey:
Cite (ACL):
Shervin Malmasi and Marcos Zampieri. 2017. Detecting Hate Speech in Social Media. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 467–472, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Detecting Hate Speech in Social Media (Malmasi & Zampieri, RANLP 2017)
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PDF:
https://doi.org/10.26615/978-954-452-049-6_062