We'd like to thank the following previous studies for making this project possible:
Khan, Heena. “Language Agnostic Model: Detecting Islamophobic Content on Social Media.” JEWLScholar@MTSU, Middle Tennessee State University, 14 April 2021, https://jewlscholar.mtsu.edu/items/9c7d6ec2-661a-40b7-a945-f63280529844.
Vidgen, Bertie, and Taha Yasseri. “Detecting Weak and Strong Islamophobic Hate Speech on Social Media,” in Journal of Information Technology & Politics, vol. 17, no. 1, 2019, pp. 66–78., doi: 10.1080/19331681.2019.1702607.
Vidgen, Bertie, et al. “Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021, doi: 10.18653/v1/2021.acl-long.132.
Y.-L. Chung, E. Kuzmenko, S. S. Tekiroglu, and M. Guerini, “CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Jul. 2019, pp. 2819–2829. doi: 10.18653/v1/P19-1271.
Z. Waseem and D. Hovy, “Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter,” in Proceedings of the NAACL Student Research Workshop, Jun. 2016, pp. 88–93. [Online]. Available: http://www.aclweb.org/anthology/N16-2013
In addition, the following sources provided data and used in our project: