A Comparative Study of Different State-of-the-Art Hate Speech Detection Methods in Hindi-English Code-Mixed Data

Priya Rani, Shardul Suryawanshi, Koustava Goswami, Bharathi Raja Chakravarthi, Theodorus Fransen, John Philip McCrae


Abstract
Hate speech detection in social media communication has become one of the primary concerns to avoid conflicts and curb undesired activities. In an environment where multilingual speakers switch among multiple languages, hate speech detection becomes a challenging task using methods that are designed for monolingual corpora. In our work, we attempt to analyze, detect and provide a comparative study of hate speech in a code-mixed social media text. We also provide a Hindi-English code-mixed data set consisting of Facebook and Twitter posts and comments. Our experiments show that deep learning models trained on this code-mixed corpus perform better.
Anthology ID:
2020.trac-1.7
Volume:
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
LREC | TRAC | WS
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
42–48
Language:
English
URL:
https://www.aclweb.org/anthology/2020.trac-1.7
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.trac-1.7.pdf