@inproceedings{rani-etal-2020-comparative,
title = "A Comparative Study of Different State-of-the-Art Hate Speech Detection Methods in {H}indi-{E}nglish Code-Mixed Data",
author = "Rani, Priya and
Suryawanshi, Shardul and
Goswami, Koustava and
Chakravarthi, Bharathi Raja and
Fransen, Theodorus and
McCrae, John Philip",
editor = "Kumar, Ritesh and
Ojha, Atul Kr. and
Lahiri, Bornini and
Zampieri, Marcos and
Malmasi, Shervin and
Murdock, Vanessa and
Kadar, Daniel",
booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/2020.trac-1.7",
pages = "42--48",
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.",
language = "English",
ISBN = "979-10-95546-56-6",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Comparative Study of Different State-of-the-Art Hate Speech Detection Methods in Hindi-English Code-Mixed Data
%A Rani, Priya
%A Suryawanshi, Shardul
%A Goswami, Koustava
%A Chakravarthi, Bharathi Raja
%A Fransen, Theodorus
%A McCrae, John Philip
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Lahiri, Bornini
%Y Zampieri, Marcos
%Y Malmasi, Shervin
%Y Murdock, Vanessa
%Y Kadar, Daniel
%S Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying
%D 2020
%8 May
%I European Language Resources Association (ELRA)
%C Marseille, France
%@ 979-10-95546-56-6
%G English
%F rani-etal-2020-comparative
%X 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.
%U https://aclanthology.org/2020.trac-1.7
%P 42-48
Markdown (Informal)
[A Comparative Study of Different State-of-the-Art Hate Speech Detection Methods in Hindi-English Code-Mixed Data](https://aclanthology.org/2020.trac-1.7) (Rani et al., TRAC 2020)
ACL