activity
20152021
most citedMitigating Gender Bias in Natural Language Processing: Literature Review

42 citations · 53 across the 5 of their papers we have counts for

collaborators

11 papers

cs.CL20213 cited

Latent Hatred: A Benchmark for Understanding Implicit Hate Speech

Mai ElSherief, Caleb Ziems, David Muchlinski +4

Hate speech has grown significantly on social media, causing serious consequences for victims of all demographics. Despite much attention being paid to characterize and detect disc…

cs.CL20216 cited

Lifelong Learning of Hate Speech Classification on Social Media

Jing Qian, Hong Wang, Mai ElSherief +1

Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. However, the amount of data in social media increases every…

cs.SI2020

Measuring and Characterizing Hate Speech on News Websites

Savvas Zannettou, Mai ElSherief, Elizabeth Belding +2

The Web has become the main source for news acquisition. At the same time, news discussion has become more social: users can post comments on news articles or discuss news articles…

cs.LG2019

Towards Understanding Gender Bias in Relation Extraction

Andrew Gaut, Tony Sun, Shirlyn Tang +8

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction (AKBC). While much attention has been dedicated…

cs.CL201942 cited

Mitigating Gender Bias in Natural Language Processing: Literature Review

Tony Sun, Andrew Gaut, Shirlyn Tang +7

As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases an…

cs.CL2019

Learning to Decipher Hate Symbols

Jing Qian, Mai ElSherief, Elizabeth Belding +1

Existing computational models to understand hate speech typically frame the problem as a simple classification task, bypassing the understanding of hate symbols (e.g., 14 words, ki…