June 10, 2024, 4:42 a.m. | Nankai Lin, Hongyan Wu, Zhengming Chen, Zijian Li, Lianxi Wang, Shengyi Jiang, Dong Zhou, Aimin Yang

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.04876v1 Announce Type: new
Abstract: Hate speech on social media is ubiquitous but urgently controlled. Without detecting and mitigating the biases brought by hate speech, different types of ethical problems. While a number of datasets have been proposed to address the problem of hate speech detection, these datasets seldom consider the diversity and variability of bias, making it far from real-world scenarios. To fill this gap, we propose a benchmark, named HateDebias, to analyze the model ability of hate speech …

abstract arxiv biases cs.cl datasets detection diversity ethical hate speech hate speech detection media problem social social media speech type types while

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