Mohammad Dehghan, Mohammad Alomrani, Sunyam Bagga, David Alfonso-Hermelo, Khalil Bibi, Abbas Ghaddar, Yingxue Zhang, Xiaoguang Li, Jianye Hao, Qun Liu, Jimmy Lin, Boxing Chen, Prasanna Parthasarathi, Mahdi Biparva, Mehdi Rezagholizadeh. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Abbas Ghaddar, Yimeng Wu, Sunyam Bagga, Ahmad Rashid, Khalil Bibi, Mehdi Rezagholizadeh, Chao Xing, Yasheng Wang, Xinyu Duan, Zhefeng Wang, Baoxing Huai, Xin Jiang, Qun Liu, Phillippe Langlais. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

Abusive language in online discourse negatively affects a large number of social media users.Many computational methods have been proposed to address this issue of online abuse.The existing work, however, tends to focus on detecting the more explicit forms of abuse leaving the subtler forms of abuse largely untouched.Our work addresses this gap by making three core contributions.First, inspired by the theory of impoliteness, we propose a novel task of detecting a subtler form of abuse, namely unpalatable questions.Second, we publish a context-aware dataset for the task using data from a diverse set of Reddit communities.Third, we implement a wide array of learning models and also investigate the benefits of incorporating conversational context into computational models.Our results show that modeling subtle abuse is feasible but difficult due to the language involved being highly nuanced and context-sensitive.We hope that future research in the field will address such subtle forms of abuse since their harm currently passes unnoticed through existing detection systems.