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README.md

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 The dataset consists 11,549 YouTube comments in Hindi-English code-mixed language for targeted hate speech detection against religion. Binary and multi-class tagging of YouTube comments is used. 
 The classification of YouTube comments addresses two subtasks: Subtask-1 (Binary classification): comments are labeled as antireligion or non-antireligion. Subtask-2 (Multi-class classification): comments are labeled on the major targeted religions such as Islam, Hinduism, and Christianity, with a ‘none’ class also provided.
 
-For more information, refer this paper: Sharma, D., Singh, A., & Singh, V. K. (2024). THAR-Targeted Hate Speech Against Religion: A high-quality Hindi-English code-mixed Dataset with the Application of Deep Learning Models for Automatic Detection. ACM Transactions on Asian and Low-Resource Language Information Processing.
+For more information, refer this paper: Sharma, D., Singh, A., & Singh, V. K. (2024). THAR-Targeted Hate Speech Against Religion: A high-quality Hindi-English code-mixed Dataset with the Application of Deep Learning Models for Automatic Detection. ACM Transactions on Asian and Low-Resource Language Information Processing.
+https://doi.org/10.1145/3653017