A comparative study of different state-of-the-art hate speech detection methods in Hindi-English code-mixed data
Rani, Priya Suryawanshi, Shardul Goswami, Koustava Chakravarthi, Bharathi Raja Fransen, Theodorus McCrae, John P.
Rani, Priya
Suryawanshi, Shardul
Goswami, Koustava
Chakravarthi, Bharathi Raja
Fransen, Theodorus
McCrae, John P.
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Publication Date
2020-05-11
Type
Workshop paper
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Rani, Priya, Suryawanshi, Shardul, Goswami, Koustava, Chakravarthi, Bharathi Raja, Fransen, Theodorus, & McCrae, John P. (2020). A comparative study of different state-of-the-art hate speech detection methods in Hindi-English code-mixed data. Paper presented at the Language Resources and Evaluation Conference (LREC 2020) Second Workshop on Trolling, Aggression and Cyberbullying, Marseille, France, 11-16 May.
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.
Publisher
European Language Resources Association (ELRA)
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Attribution-NonCommercial-NoDerivs 3.0 Ireland