1 code implementation • EMNLP 2021 • Mai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, Diyi Yang
Hate speech has grown significantly on social media, causing serious consequences for victims of all demographics.
no code implementations • NAACL 2021 • Jing Qian, Hong Wang, Mai ElSherief, Xifeng Yan
In this work, we propose lifelong learning of hate speech classification on social media.
1 code implementation • ACL 2020 • Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
We use WikiGenderBias to evaluate systems for bias and find that NRE systems exhibit gender biased predictions and lay groundwork for future evaluation of bias in NRE.
1 code implementation • ACL 2019 • Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP.
no code implementations • NAACL 2019 • Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang
Furthermore, we propose a novel Variational Decipher and show how it can generalize better to unseen hate symbols in a more challenging testing setting.
no code implementations • EMNLP 2018 • Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang
Existing work on automated hate speech detection typically focuses on binary classification or on differentiating among a small set of categories.
2 code implementations • 11 Apr 2018 • Mai ElSherief, Vivek Kulkarni, Dana Nguyen, William Yang Wang, Elizabeth Belding
While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech.
no code implementations • NAACL 2018 • Jing Qian, Mai ElSherief, Elizabeth M. Belding, William Yang Wang
Hate speech detection is a critical, yet challenging problem in Natural Language Processing (NLP).